{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:30:44Z","timestamp":1772253044020,"version":"3.50.1"},"reference-count":154,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall\u2013runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall\u2013runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall\u2013runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash\u2013Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3\/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and \u22120.20 during the training period, respectively, and 5.53, 0.95, 0.92, and \u22120.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models\u2019 training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.<\/jats:p>","DOI":"10.3390\/su14138209","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:15:52Z","timestamp":1657142152000},"page":"8209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["An Integrated Statistical-Machine Learning Approach for Runoff Prediction"],"prefix":"10.3390","volume":"14","author":[{"given":"Abhinav Kumar","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8013-5663","authenticated-orcid":false,"given":"Pankaj","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4903-7720","authenticated-orcid":false,"given":"Rawshan","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Petroleum, Koya Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6790-2653","authenticated-orcid":false,"given":"Nadhir","family":"Al-Ansari","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-6995","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Vishwakarma","sequence":"additional","affiliation":[{"name":"Department of Irrigation and Drainage Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India"}]},{"given":"Kuldeep Singh","family":"Kushwaha","sequence":"additional","affiliation":[{"name":"Centre for Water Engineering and Management, Central University of Jharkhand, Ranchi 835205, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5482-7781","authenticated-orcid":false,"given":"Kanhu Charan","family":"Panda","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3597-385X","authenticated-orcid":false,"given":"Atish","family":"Sagar","sequence":"additional","affiliation":[{"name":"Division of Agricultural Engineering, ICAR\u2014Indian Agriculture Research Institute, New Delhi 110012, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9252-2241","authenticated-orcid":false,"given":"Ehsan","family":"Mirzania","sequence":"additional","affiliation":[{"name":"Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-9502","authenticated-orcid":false,"given":"Ahmed","family":"Elbeltagi","sequence":"additional","affiliation":[{"name":"Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-8377","authenticated-orcid":false,"given":"Alban","family":"Kuriqi","sequence":"additional","affiliation":[{"name":"CERIS, Instituto Superior T\u00e9cnico, University of Lisbon, 1649-004 Lisbon, Portugal"},{"name":"Civil Engineering Department, University for Business and Technology, 10000 Pristina, Kosovo"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8055-8463","authenticated-orcid":false,"given":"Salim","family":"Heddam","sequence":"additional","affiliation":[{"name":"Laboratory of Research in Biodiversity 17 Interaction Ecosystem and Biotechnology, Agronomy Department, Hydraulics Division, Faculty of Science, University 20 Ao\u00fbt 1955, Route El Hadaik, Skikda 21000, Algeria"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1080\/02626667.2022.2027951","article-title":"Improving the outputs of regional heavy rainfall forecasting models using an adaptive real-time approach","volume":"67","author":"Alizadeh","year":"2022","journal-title":"Hydrol. Sci. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khan, M.T., Shoaib, M., Hammad, M., Salahudin, H., Ahmad, F., and Ahmad, S. (2021). Application of machine learning techniques in rainfall\u2013runoff modelling of the soan river basin, Pakistan. Water, 13.","DOI":"10.3390\/w13243528"},{"key":"ref_3","first-page":"100204","article-title":"Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting","volume":"7","author":"Oyedele","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Basha, C.Z., Bhavana, N., Bhavya, P., and Sowmya, V. (2020, January 2\u20134). Rainfall prediction using machine learning & deep learning techniques. Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India.","DOI":"10.1109\/ICESC48915.2020.9155896"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.jhydrol.2014.11.028","article-title":"Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons","volume":"520","author":"Yang","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1007\/s11069-015-1643-8","article-title":"A real-time flood forecasting system with dual updating of the NWP rainfall and the river flow","volume":"77","author":"Liu","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Ozturk, P., and Chau, K. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10.","DOI":"10.20944\/preprints201810.0098.v2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.jhydrol.2014.02.047","article-title":"The examination of reproducibility in hydro-ecological characteristics by daily synthetic flow models","volume":"511","author":"You","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_9","first-page":"1191","article-title":"Development of 48-hour precipitation forecasting model using nonlinear autoregressive neural network","volume":"Volume 54","author":"Benboudjema","year":"2020","journal-title":"Innovation for Sustainable Infrastructure"},{"key":"ref_10","first-page":"9","article-title":"Estimation and validation of runoff and sediment models for Dachigam watershed of Kashmir Valley","volume":"43","author":"Amin","year":"2015","journal-title":"Indian J. Soil Conserv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kumar, R., Manzoor, S., Vishwakarma, D.K., Al-Ansari, N., Kushwaha, N.L., Elbeltagi, A., Sushanth, K., Prasad, V., and Kuriqi, A. (2022). Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region. Sustainability, 14.","DOI":"10.3390\/su14031150"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.20546\/ijcmas.2018.705.302","article-title":"Modeling of Rainfall and Ground Water Fluctuation of Gonda District Uttar Pradesh, India","volume":"7","author":"Vishwakarma","year":"2018","journal-title":"Int. J. Curr. Microbiol. Appl. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/ird.2108","article-title":"Efficient Design of Drip Irrigation System using Water and Fertilizer Application Uniformity at Different Operating Pressures in a Semi-Arid Region of India","volume":"66","author":"Kumar","year":"2017","journal-title":"Irrig. Drain."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10584-006-9205-4","article-title":"Adaptation to climate change and variability: Farmer responses to intra-seasonal precipitation trends in South Africa","volume":"83","author":"Thomas","year":"2007","journal-title":"Clim. Chang."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e23524","DOI":"10.1002\/ajhb.23524","article-title":"Scaling climate change to human behavior predicting good and bad years for Maya farmers","volume":"33","author":"Kramer","year":"2021","journal-title":"Am. J. Hum. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Ma, X., Liang, L., and Yao, W. (2020). Spatial\u2013Temporal Variation Characteristics of Multiple Meteorological Variables and Vegetation over the Loess Plateau Region. Appl. Sci., 10.","DOI":"10.3390\/app10031000"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123959","DOI":"10.1016\/j.jhydrol.2019.123959","article-title":"A novel Master\u2013Slave optimization algorithm for generating an optimal release policy in case of reservoir operation","volume":"577","author":"Turgut","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125133","DOI":"10.1016\/j.jhydrol.2020.125133","article-title":"Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization","volume":"589","author":"Tikhamarine","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Banadkooki, F.B., Ehteram, M., Ahmed, A.N., Fai, C.M., Afan, H.A., Ridwam, W.M., Sefelnasr, A., and El-Shafie, A. (2019). Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models. Sustainability, 11.","DOI":"10.3390\/su11236681"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1080\/02626667.2019.1678750","article-title":"Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches","volume":"64","author":"Tikhamarine","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.jhydrol.2016.12.024","article-title":"Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques","volume":"545","author":"Chang","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1061\/(ASCE)1084-0699(1999)4:3(232)","article-title":"Rainfall-runoff modeling using artificial neural networks","volume":"4","author":"Tokar","year":"1999","journal-title":"J. Hydrol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1007\/s10661-021-09499-9","article-title":"Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models","volume":"193","author":"Kawanisi","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e1471","DOI":"10.1002\/wat2.1471","article-title":"Historical development of rainfall-runoff modeling","volume":"7","author":"Peel","year":"2020","journal-title":"WIREs Water"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-540-77843-1_1","article-title":"General review of rainfall-runoff modeling: Model calibration, data assimilation, and uncertainty analysis","volume":"Volume 63","author":"Sorooshian","year":"2008","journal-title":"Hydrological Modelling and the Water Cycle"},{"key":"ref_26","unstructured":"Daniell, T.M. Neural networks. Applications in hydrology and water resources engineering. Proceedings of the National Conference Publication\u2014Institute of Engineers, Perth, Australia."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0022-1694(92)90046-X","article-title":"Rainfall forecasting in space and time using a neural network","volume":"137","author":"French","year":"1992","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Asadi, H., Shahedi, K., Jarihani, B., and Sidle, R.C. (2019). Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach. Water, 11.","DOI":"10.3390\/w11020212"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compeleceng.2018.06.004","article-title":"Rainfall prediction for the Kerala state of India using artificial intelligence approaches","volume":"70","author":"Dash","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"458","DOI":"10.2166\/hydro.2010.032","article-title":"A hybrid model coupled with singular spectrum analysis for daily rainfall prediction","volume":"12","author":"Chau","year":"2010","journal-title":"J. Hydroinform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1016\/j.asoc.2013.07.007","article-title":"Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff","volume":"13","author":"Kisi","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1111\/j.1752-1688.2012.00642.x","article-title":"Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1","volume":"48","author":"Harshburger","year":"2012","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.jhydrol.2016.06.026","article-title":"A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network","volume":"540","author":"Humphrey","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jhydrol.2010.12.041","article-title":"A wavelet-support vector machine conjunction model for monthly streamflow forecasting","volume":"399","author":"Kisi","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Thapa, S., Zhao, Z., Li, B., Lu, L., Fu, D., Shi, X., Tang, B., and Qi, H. (2020). Snowmelt-Driven Streamflow Prediction Using Machine Learning Techniques (LSTM, NARX, GPR, and SVR). Water, 12.","DOI":"10.3390\/w12061734"},{"key":"ref_36","unstructured":"Chalup, S.K., Blair, A.D., and Randall, M. (2015). Wavelet based artificial intelligence approaches for prediction of hydrological time series. Artificial Life and Computational Intelligence. ACALCI 2015, Springer. Lecture Notes in Computer Science."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s00477-021-01982-6","article-title":"Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir","volume":"35","author":"Idrees","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.jhydrol.2012.11.048","article-title":"Daily suspended sediment load prediction using artificial neural networks and support vector machines","volume":"478","author":"Ahmadi","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.1016\/j.scitotenv.2009.05.016","article-title":"Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models","volume":"407","author":"Rajaee","year":"2009","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.agwat.2010.12.012","article-title":"Suspended sediment load prediction of river systems: An artificial neural network approach","volume":"98","author":"Melesse","year":"2011","journal-title":"Agric. Water Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1007\/s12665-021-09625-3","article-title":"Artificial intelligence for suspended sediment load prediction: A review","volume":"80","author":"Gupta","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1007\/s12517-012-0608-4","article-title":"Suspended sediment load prediction of river systems: GEP approach","volume":"6","author":"Azamathulla","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., and Chen, S.-T. (2020). Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods. Water, 12.","DOI":"10.3390\/w12030787"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s13201-014-0258-7","article-title":"Modeling of stage\u2013discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi\u2013Sugeno inference system technique: A comparative study","volume":"6","year":"2016","journal-title":"Appl. Water Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1002\/clen.200800010","article-title":"Modeling River Stage-Discharge Relationships Using Different Neural Network Computing Techniques","volume":"37","author":"Kisi","year":"2009","journal-title":"Clean Soil Air Water"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2006.05.007","article-title":"Takagi\u2013Sugeno fuzzy inference system for modeling stage\u2013discharge relationship","volume":"331","author":"Lohani","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s00704-021-03863-y","article-title":"Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques","volume":"147","author":"Shukla","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5702","DOI":"10.1016\/j.eswa.2011.11.101","article-title":"Development of stage\u2013discharge rating curve using model tree and neural networks: An application to Peachtree Creek in Atlanta","volume":"39","author":"Ajmera","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1002\/met.1661","article-title":"Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network","volume":"24","author":"Araghi","year":"2017","journal-title":"Meteorol. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","article-title":"Estimation of soil temperature from meteorological data using different machine learning models","volume":"338","author":"Feng","year":"2019","journal-title":"Geoderma"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s00703-010-0104-x","article-title":"Prediction of soil temperature using regression and artificial neural network models","volume":"110","author":"Bilgili","year":"2010","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_52","first-page":"119","article-title":"Haar wavelet in estimating depth profile of soil temperature","volume":"210","author":"Hariharan","year":"2009","journal-title":"Appl. Math. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.compag.2018.04.019","article-title":"Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area","volume":"150","author":"Singh","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"104513","DOI":"10.1016\/j.still.2019.104513","article-title":"Developing novel hybrid models for estimation of daily soil temperature at various depths","volume":"197","author":"Mehdizadeh","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s00704-012-0807-7","article-title":"Spatiotemporal modeling of monthly soil temperature using artificial neural networks","volume":"113","author":"Wu","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10723","DOI":"10.1007\/s00500-021-06009-4","article-title":"GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables","volume":"25","author":"Seifi","year":"2021","journal-title":"Soft Comput."},{"key":"ref_57","first-page":"724","article-title":"Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran","volume":"12","author":"Kazempour","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s00521-012-1027-x","article-title":"Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system","volume":"23","author":"Terzi","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s00271-010-0225-5","article-title":"Daily pan evaporation modeling using linear genetic programming technique","volume":"29","author":"Guven","year":"2011","journal-title":"Irrig. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kushwaha, N.L., Rajput, J., Elbeltagi, A., Elnaggar, A.Y., Sena, D.R., Vishwakarma, D.K., Mani, I., and Hussein, E.E. (2021). Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India. Atmosphere, 12.","DOI":"10.3390\/atmos12121654"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1061\/(ASCE)HE.1943-5584.0000056","article-title":"Daily Pan Evaporation Modeling in a Hot and Dry Climate","volume":"14","author":"Piri","year":"2009","journal-title":"J. Hydrol. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Shabani, S., Samadianfard, S., Sattari, M.T., Shamshirband, S., Mosavi, A., Kmet, T., and V\u00e1rkonyi-K\u00f3czy, A.R. (2019). Modeling daily pan evaporation in humid climates using Gaussian Process Regression. arXiv.","DOI":"10.20944\/preprints201907.0351.v1"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1080\/02626667.2014.945937","article-title":"Predicting daily pan evaporation by soft computing models with limited climatic data","volume":"60","author":"Kim","year":"2015","journal-title":"Hydrol. Sci. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.compag.2016.05.018","article-title":"A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method","volume":"127","author":"Keshtegar","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kumar, M., Kumari, A., Kumar, D., Al-Ansari, N., Ali, R., Kumar, R., Kumar, A., Elbeltagi, A., and Kuriqi, A. (2021). The superiority of data-driven techniques for estimation of daily pan evaporation. Atmosphere, 12.","DOI":"10.3390\/atmos12060701"},{"key":"ref_66","first-page":"1075","article-title":"Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test","volume":"15","author":"Malik","year":"2021","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s00271-009-0201-0","article-title":"Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression","volume":"28","author":"Tabari","year":"2010","journal-title":"Irrig. Sci."},{"key":"ref_68","first-page":"467","article-title":"Daily pan evaporation modeling in hilly region of Uttarakhand using artificial neural network","volume":"44","author":"Bhagwat","year":"2017","journal-title":"Indian J. Ecol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.jhydrol.2019.04.085","article-title":"Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions","volume":"574","author":"Huang","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"124434","DOI":"10.1016\/j.jhydrol.2019.124434","article-title":"Multi-step ahead modeling of reference evapotranspiration using a multi-model approach","volume":"581","author":"Nourani","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1007\/s11269-015-0990-2","article-title":"Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration with Limited Climatic Data in Extreme Arid Regions","volume":"29","author":"Wen","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_72","first-page":"42","article-title":"Time series modelling of monthly reference evapotranspiration for Bikaner, Rajasthan (India)","volume":"46","author":"Mor","year":"2018","journal-title":"Indian J. Soil Conserv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compag.2017.01.027","article-title":"Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data","volume":"136","author":"Feng","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Dong, L., Zeng, W., Wu, L., Lei, G., Chen, H., Srivastava, A.K., and Gaiser, T. (2021). Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm. Water, 13.","DOI":"10.3390\/w13030256"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","article-title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","volume":"263","author":"Fan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.jhydrol.2007.12.014","article-title":"Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling","volume":"351","author":"Kim","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s13201-022-01667-7","article-title":"Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration","volume":"12","author":"Elbeltagi","year":"2022","journal-title":"Appl. Water Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Elbeltagi, A., Kushwaha, N.L., Rajput, J., Vishwakarma, D.K., Kulimushi, L.C., Kumar, M., Zhang, J., Pande, C.B., Choudhari, P., and Meshram, S.G. (2022). Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions. Stoch. Environ. Res. Risk Assess.","DOI":"10.1007\/s00477-022-02196-0"},{"key":"ref_79","first-page":"1082","article-title":"Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity","volume":"16","author":"Singh","year":"2022","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s40808-017-0357-1","article-title":"Modelling of infiltration of sandy soil using gaussian process regression","volume":"3","author":"Sihag","year":"2017","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/09715010.2017.1381861","article-title":"Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS)","volume":"25","author":"Sihag","year":"2019","journal-title":"ISH J. Hydraul. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.wsj.2017.03.001","article-title":"Estimation and inter-comparison of infiltration models","volume":"31","author":"Sihag","year":"2017","journal-title":"Water Sci."},{"key":"ref_83","first-page":"109","article-title":"Comparative analysis of artificial intelligence techniques for the prediction of infiltration process","volume":"5","author":"Singh","year":"2021","journal-title":"Geol. Ecol. Landsc."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/09715010.2018.1464408","article-title":"Modeling the infiltration process with soft computing techniques","volume":"26","author":"Sihag","year":"2020","journal-title":"ISH J. Hydraul. Eng."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/09715010.2019.1574615","article-title":"Estimation of permeability of soil using easy measured soil parameters: Assessing the artificial intelligence-based models","volume":"27","author":"Singh","year":"2021","journal-title":"ISH J. Hydraul. Eng."},{"key":"ref_86","first-page":"241","article-title":"Assessment of infiltration models developed using soft computing techniques","volume":"5","author":"Sihag","year":"2021","journal-title":"Geol. Ecol. Landsc."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1080\/02626667.2019.1659965","article-title":"Modelling of infiltration using artificial intelligence techniques in semi-arid Iran","volume":"64","author":"Sihag","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"124223","DOI":"10.1016\/j.jhydrol.2019.124223","article-title":"Modelling of soil permeability using different data driven algorithms based on physical properties of soil","volume":"580","author":"Singh","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"17591","DOI":"10.1007\/s11356-021-17064-7","article-title":"Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India","volume":"29","author":"Elbeltagi","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s11269-021-02969-2","article-title":"Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence","volume":"36","author":"Gholami","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"106625","DOI":"10.1016\/j.agwat.2020.106625","article-title":"Groundwater quality forecasting using machine learning algorithms for irrigation purposes","volume":"245","author":"Taleb","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"130265","DOI":"10.1016\/j.chemosphere.2021.130265","article-title":"Prediction of groundwater quality using efficient machine learning technique","volume":"276","author":"Singha","year":"2021","journal-title":"Chemosphere"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Kumar, A., Singh, V.K., Saran, B., Al-Ansari, N., Singh, V.P., Adhikari, S., Joshi, A., Singh, N.K., and Vishwakarma, D.K. (2022). Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Sustainability, 14.","DOI":"10.20944\/preprints202201.0415.v1"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"107052","DOI":"10.1016\/j.agwat.2021.107052","article-title":"Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt","volume":"255","author":"Elbeltagi","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"106080","DOI":"10.1016\/j.agwat.2020.106080","article-title":"Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt","volume":"235","author":"Elbeltagi","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1002\/ird.2566","article-title":"Artificial intelligence approach to estimating rice yield*","volume":"70","author":"Babaee","year":"2021","journal-title":"Irrig. Drain."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"105368","DOI":"10.1016\/j.compag.2020.105368","article-title":"Modeling monthly crop coefficients of maize based on limited meteorological data: A case study in Nile Delta, Egypt","volume":"173","author":"Elbeltagi","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.28991\/cej-2019-03091398","article-title":"A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling","volume":"5","author":"Kumar","year":"2019","journal-title":"Civ. Eng. J."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s00376-012-1259-9","article-title":"Application of artificial neural networks to rainfall forecasting in Queensland, Australia","volume":"29","author":"Abbot","year":"2012","journal-title":"Adv. Atmos. Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.jhydrol.2016.01.076","article-title":"A comparison between wavelet based static and dynamic neural network approaches for runoff prediction","volume":"535","author":"Shoaib","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.aej.2012.01.005","article-title":"Runoff forecasting by artificial neural network and conventional model","volume":"50","author":"Ghumman","year":"2011","journal-title":"Alex. Eng. J."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"W07415","DOI":"10.1029\/2006WR004930","article-title":"Rainfall-runoff modeling through hybrid intelligent system","volume":"43","author":"Nayak","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_103","unstructured":"Peterson, P., Baker, E., and McGaw, B. (2010). An Overview of statistics in education. International Encyclopedia of Education, Elsevier. [3rd ed.]."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"107378","DOI":"10.1016\/j.agwat.2021.107378","article-title":"Methods to estimate evapotranspiration in humid and subtropical climate conditions","volume":"261","author":"Vishwakarma","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/BF03326090","article-title":"Evaluation of ground water quality using multiple linear regression and structural equation modeling","volume":"6","author":"Chenini","year":"2009","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_106","unstructured":"Snedecor, G.W., Cochran, W.G., and Fuller, J.A.R. (1971). M\u00e9todos Estad\u00edsticos, Continental."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1016\/j.rser.2017.05.249","article-title":"Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM","volume":"82","author":"Roy","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s11240-019-01763-8","article-title":"Analysis of macro nutrient related growth responses using multivariate adaptive regression splines","volume":"140","author":"Akin","year":"2020","journal-title":"Plant Cell Tissue Organ Cult."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"6843","DOI":"10.1007\/s00521-018-3519-9","article-title":"Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models","volume":"31","author":"Mirabbasi","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.asej.2020.10.010","article-title":"Effects of rainfall and runoff-yield conditions on runoff","volume":"12","author":"Zhang","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_111","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, John Wiley & Sons, Inc."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"125168","DOI":"10.1016\/j.jhydrol.2020.125168","article-title":"Estimating annual runoff in response to forest change: A statistical method based on random forest","volume":"589","author":"Li","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2019.03.004","article-title":"Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation","volume":"573","author":"Salih","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1007\/s11069-020-04438-2","article-title":"Comparison of different methodologies for rainfall\u2013runoff modeling: Machine learning vs conceptual approach","volume":"105","author":"Adnan","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1080\/02626667.2019.1680846","article-title":"Comparison of daily streamflow forecasts using extreme learning machines and the random forest method","volume":"64","author":"Li","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"5267","DOI":"10.1016\/j.eswa.2014.02.047","article-title":"Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS","volume":"41","author":"Goyal","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compag.2017.04.005","article-title":"Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India","volume":"138","author":"Malik","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_118","first-page":"323","article-title":"Modeling monthly pan evaporation process over the Indian central Himalayas: Application of multiple learning artificial intelligence model","volume":"14","author":"Malik","year":"2020","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s12517-018-3614-3","article-title":"Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test","volume":"11","author":"Singh","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/BF01413858","article-title":"A note on the Gamma test","volume":"5","author":"Jones","year":"1997","journal-title":"Neural Comput. Appl."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jhydrol.2011.02.021","article-title":"Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction","volume":"401","author":"Noori","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s12665-018-7892-6","article-title":"Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques","volume":"77","author":"Singh","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_123","unstructured":"Singh, V.K., Kumar, D., Kashyap, P.S., and Singh, P.K. (2019, January 15\u201316). Predicting unsaturated hydraulic conductivity of soil based on machine learning algorithms. Proceedings of the International Conference on Opportunities and Challenges in Engineering, Management and Science (OCEMS\u20142019), Bareilly, India."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.enggeo.2015.01.009","article-title":"Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines","volume":"188","author":"Zhang","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_125","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jhydrol.2015.12.014","article-title":"Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution","volume":"534","author":"Kisi","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Zhang, X. (2017). Matrix Analysis and Applications, Cambridge University Press.","DOI":"10.1017\/9781108277587"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"3843","DOI":"10.1007\/s11269-017-1711-9","article-title":"Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non\u2013Parametric Paradigm vs. Model Classification Methods","volume":"31","author":"Zahmatkesh","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"123981","DOI":"10.1016\/j.jhydrol.2019.123981","article-title":"Daily streamflow prediction using optimally pruned extreme learning machine","volume":"577","author":"Adnan","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"265","DOI":"10.2166\/hydro.2004.0020","article-title":"Identification of support vector machines for runoff modelling","volume":"6","author":"Bray","year":"2004","journal-title":"J. Hydroinform."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support vector regression. Efficient Learning Machines, Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Kumar, M., Kumari, A., Kushwaha, D.P., Kumar, P., Malik, A., Ali, R., and Kuriqi, A. (2020). Estimation of Daily Stage\u2013Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India. Sustainability, 12.","DOI":"10.3390\/su12197877"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jhydrol.2018.01.044","article-title":"Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest","volume":"559","author":"Sadler","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1007\/s11269-015-0915-0","article-title":"Pan Evaporation Simulation Based on Daily Meteorological Data Using Soft Computing Techniques and Multiple Linear Regression","volume":"29","author":"Malik","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"101595","DOI":"10.1016\/j.asej.2021.09.022","article-title":"Modelling of meteorological drought in the foothills of Central Himalayas: A case study in Uttarakhand State, India","volume":"13","author":"Kumar","year":"2022","journal-title":"Ain Shams Eng. J."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"4018023","DOI":"10.1061\/(ASCE)IR.1943-4774.0001336","article-title":"Daily pan evaporation estimation using heuristic methods with gamma test","volume":"144","author":"Malik","year":"2018","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jksus.2015.12.002","article-title":"Bin Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh","volume":"29","author":"Nury","year":"2017","journal-title":"J. King Saud Univ. Sci."},{"key":"ref_142","unstructured":"Wooldridge, M. (2009). An Introduction to Multiagent Systems, John Wiley & Sons."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1002\/1520-6297(198624)2:4<501::AID-AGR2720020412>3.0.CO;2-G","article-title":"Managing innovation and change processes: Findings from the Minnesota innovation research program","volume":"2","author":"Schroeder","year":"1986","journal-title":"Agribusiness"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1111\/j.1752-1688.2001.tb03630.x","article-title":"Validation of the swat model on a large rwer basin with point and nonpoint sources","volume":"37","author":"Santhi","year":"2001","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.13031\/2013.15643","article-title":"Hydrologic simulation on agricultural watersheds: Choosing between two models","volume":"46","author":"Arnold","year":"2003","journal-title":"Trans. ASAE"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1029\/1998WR900018","article-title":"Evaluating the use of \u201cgoodness-of-fit\u201d Measures in hydrologic and hydroclimatic model validation","volume":"35","author":"Legates","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1061\/(ASCE)1084-0699(1999)4:2(135)","article-title":"Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration","volume":"4","author":"Gupta","year":"1999","journal-title":"J. Hydrol. Eng."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model evaluation guidelines for systematic quantification of accuracy in watershed simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.wse.2018.09.008","article-title":"A regional suspended load yield estimation model for ungauged watersheds","volume":"11","author":"Kheirfam","year":"2018","journal-title":"Water Sci. Eng."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11269-017-1660-3","article-title":"Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping","volume":"31","author":"Naghibi","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"127381","DOI":"10.1016\/j.jhydrol.2021.127381","article-title":"Representative grid location-multivariate adaptive regression spline (RGL-MARS) algorithm for downscaling dry and wet season rainfall","volume":"605","author":"Panda","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.conbuildmat.2019.03.189","article-title":"Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression","volume":"210","author":"Zhang","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3847\/1538-3881\/aaf101","article-title":"Probabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets","volume":"157","author":"Reis","year":"2018","journal-title":"Astron. J."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s10489-021-02451-x","article-title":"Lightweight surrogate random forest support for model simplification and feature relevance","volume":"52","author":"Kim","year":"2022","journal-title":"Appl. Intell."}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/14\/13\/8209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:43:06Z","timestamp":1760139786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/14\/13\/8209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,5]]},"references-count":154,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["su14138209"],"URL":"https:\/\/doi.org\/10.3390\/su14138209","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202206.0163.v1","asserted-by":"object"}]},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,5]]}}}