{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:29:47Z","timestamp":1767846587940,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Railroad Administration","award":["693JJ6-21-C-000004"],"award-info":[{"award-number":["693JJ6-21-C-000004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center\u2013River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts.<\/jats:p>","DOI":"10.3390\/rs16132332","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T07:20:06Z","timestamp":1719386406000},"page":"2332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automated Flood Prediction along Railway Tracks Using Remotely Sensed Data and Traditional Flood Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3694-7082","authenticated-orcid":false,"given":"Abdul-Rashid","family":"Zakaria","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, The University of Mississippi, 201 Weir Hall, University, Oxford, MS 38677, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-3474","authenticated-orcid":false,"given":"Thomas","family":"Oommen","sequence":"additional","affiliation":[{"name":"Department of Geological and Geological Engineering, The University of Mississippi, 120 A Carrier Hall, University, Oxford, MS 38677, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5552-5590","authenticated-orcid":false,"given":"Pasi","family":"Lautala","sequence":"additional","affiliation":[{"name":"Civil, Environmental and Geospatial Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bell, L., and Bell, F.G. (1999). Geological Hazards: Their Assessment, Avoidance and Mitigation, CRC Press LLC.","DOI":"10.4324\/9780203014660"},{"key":"ref_2","unstructured":"USACE (2016). Yellowstone River Corridor Study Hydraulic Analysis Modeling and Mapping Report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1061\/(ASCE)1084-0699(2001)6:2(91)","article-title":"Distributed watershed model compatible with remote sensing and GIS data. I: Description of model","volume":"6","author":"Fortin","year":"2001","journal-title":"J. Hydrol. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/hyp.5624","article-title":"Advances in the application of the SWAT model for water resources management","volume":"19","author":"Jayakrishnan","year":"2005","journal-title":"Hydrol. Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jhydrol.2013.09.034","article-title":"Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS","volume":"504","author":"Tehrany","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jhydrol.2016.06.027","article-title":"Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS","volume":"540","author":"Pradhan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1016\/j.scitotenv.2018.12.034","article-title":"Impact of land use changes on flash flood prediction using a sub-daily SWAT model in five Mediterranean ungauged watersheds (SE Spain)","volume":"657","author":"Pla","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kastridis, A., and Stathis, D. (2020). Evaluation of hydrological and hydraulic models applied in typical Mediterranean Ungauged watersheds using post-flash-flood measurements. Hydrology, 7.","DOI":"10.3390\/hydrology7010012"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1111\/j.1744-1714.1975.tb01426.x","article-title":"The Flood Disaster Protection Act of 1973","volume":"13","year":"1976","journal-title":"Am. Bus. Law J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.scitotenv.2015.08.055","article-title":"Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope\u2013Evros region, Greece","volume":"538","author":"Kazakis","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, M.J., Kang, J.E., and Jeon, S. (2012, January 22\u201327). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351414"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TGRS.1990.572944","article-title":"Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data","volume":"28","author":"Benediktsson","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00477-015-1021-9","article-title":"Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method","volume":"29","author":"Tehrany","year":"2015","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.catena.2014.10.017","article-title":"Flood susceptibility assessment using GIS-based support vector machine model with different kernel types","volume":"125","author":"Tehrany","year":"2015","journal-title":"CATENA"},{"key":"ref_15","unstructured":"Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I., and Dick, O.B. (2012, January 1\u20135). Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. Proceedings of the iEMSs 2012-Managing Resources of a Limited Planet, 6th Biennial Meeting of the International Environmental Modelling and Software Society, Leipzig, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12665-011-1504-z","article-title":"An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia","volume":"67","author":"Kia","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Konadu, D., and Fosu, C. (2009). Digital elevation models and GIS for watershed modelling and flood prediction\u2013a case study of Accra Ghana. Appropriate Technologies for Environmental Protection in the Developing World, Springer.","DOI":"10.1007\/978-1-4020-9139-1_31"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111664","DOI":"10.1016\/j.rse.2020.111664","article-title":"Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine","volume":"240","author":"DeVries","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1080\/19475705.2018.1543212","article-title":"Satellite-based assessment of the August 2018 flood in parts of Kerala, India","volume":"10","author":"Vishnu","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s12524-009-0002-1","article-title":"Flood inundation modeling using MIKE FLOOD and remote sensing data","volume":"37","author":"Patro","year":"2009","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s12145-022-00904-6","article-title":"Flood modeling through remote sensing datasets such as LPRM soil moisture and GPM-IMERG precipitation: A case study of ungauged basins across Morocco","volume":"16","author":"Ouaba","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.2166\/nh.2016.133","article-title":"Assessing the impact of arid area urbanization on flash floods using GIS, remote sensing, and HEC-HMS rainfall-runoff modeling","volume":"47","year":"2016","journal-title":"Hydrol. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TGRS.2010.2057513","article-title":"Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins","volume":"49","author":"Khan","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/02626667.2011.555836","article-title":"Flood management and a GIS modelling method to assess flood-hazard areas\u2014A case study","volume":"56","author":"Kourgialas","year":"2011","journal-title":"Hydrol. Sci. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0022-1694(03)00242-7","article-title":"A diffusive transport approach for flow routing in GIS-based flood modeling","volume":"283","author":"Liu","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_26","unstructured":"Mason, L.A. (2007). GIS Modeling of Riparian Zones Utilizing Digital Elevation Models and Flood Height Data. [Master\u2019s Thesis, Michigan Technological University]."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schanze, J., Zeman, E., and Marsalek, J. (2006). Flood Risk Management: Hazards, Vulnerability and Mitigation Measures, 1st. ed., Springer.","DOI":"10.1007\/978-1-4020-4598-1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.5194\/isprs-archives-XLI-B8-175-2016","article-title":"3D GIS for flood modelling in river valleys","volume":"XLI-B8","author":"Tymkow","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ighile, E.H., Shirakawa, H., and Tanikawa, H. (2022). Application of GIS and Machine Learning to Predict Flood Areas in Nigeria. Sustainability, 14.","DOI":"10.3390\/su14095039"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102154","DOI":"10.1016\/j.ijdrr.2021.102154","article-title":"A mixed approach for urban flood prediction using Machine Learning and GIS","volume":"56","author":"Motta","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1038\/s41598-023-29292-7","article-title":"Automated machine learning recognition to diagnose flood resilience of railway switches and crossings","volume":"13","author":"Sresakoolchai","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Elkhrachy, I. (2022). Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14030440"},{"key":"ref_33","unstructured":"Zelt, R.B. (1999). Environmental Setting of the Yellowstone River Basin, Montana, North Dakota, and Wyoming."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chase, K.J. (2014). Streamflow Statistics for Unregulated and Regulated Conditions for Selected Locations on the Yellowstone, Tongue, and Powder Rivers, Montana, 1928\u20132002.","DOI":"10.3133\/sir20145115"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"04022008","DOI":"10.1061\/JSWBAY.0000989","article-title":"Testing a Watershed-Scale Stream Power Index Tool for Erosion Risk Assessment in an Urban River","volume":"8","author":"Papangelakis","year":"2022","journal-title":"J. Sustain. Water Built Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"207","DOI":"10.26471\/cjees\/2022\/017\/215","article-title":"Vulnerable areas, the stream power index and the soil characteristics on the southern slope of the lipovei hills","volume":"17","author":"Micu","year":"2022","journal-title":"Carpathian J. Earth Environ. Sci."},{"key":"ref_37","unstructured":"Cobin, P.F. (2013). Probablistic Modeling of Rainfall Induced landslide Hazard Assessment in San Juan La Laguna, Solol\u00e1, Guatemala. [Master\u2019s Thesis, Michigan Technological University]."},{"key":"ref_38","first-page":"139","article-title":"Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy","volume":"32","author":"Hong","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5194\/hess-10-101-2006","article-title":"On the calculation of the topographic wetness index: Evaluation of different methods based on field observations","volume":"10","author":"Sorensen","year":"2006","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_40","unstructured":"Andrews, D.A., Lambert, G.S., and Stose, G.W. (1944). Geologic Map of Montana, Report 25."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/10106040308542289","article-title":"Geomorphological Manifestations of the Flood Hazard: A Remote Sensing Based Approach","volume":"18","author":"Jain","year":"2003","journal-title":"Geocarto Int."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3354\/cr00936","article-title":"The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology","volume":"46","author":"Pettorelli","year":"2011","journal-title":"Clim. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J. (2019). GPM IMERG Late Precipitaion L3 1 Day 0.1 Degree x 0.1 Degree V06."},{"key":"ref_45","unstructured":"UN-SPIDER (2022, October 13). In Detail: Recommended Practice: Flood Mapping and Damage Assessment Using Sentinel-1 SAR Data in Google Earth Engine. Available online: https:\/\/un-spider.org\/advisory-support\/recommended-practices\/recommended-practice-google-earth-engine-flood-mapping\/in-detail."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer. [1st ed.].","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2021). Machine Learning, Springer.","DOI":"10.1007\/978-981-15-1967-3"},{"key":"ref_48","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley. Adaptive and Learning Systems for Signal Processing, Communications, and Control."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"James, G. (2021). An Introduction to Statistical Learning: With Applications in R, Springer. [2nd ed.]. Springer Texts in Statistics.","DOI":"10.1007\/978-1-0716-1418-1"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lam, H.K., Nguyen, H.T., and Ling, S.S.H. (2012). Computational Intelligence and Its Applications Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, Imperial College Press.","DOI":"10.1142\/9781848166929"},{"key":"ref_51","unstructured":"Haykin, S.S. (1999). Neural Networks: A Comprehensive Foundation, Prentice Hall. [2nd ed.]."},{"key":"ref_52","unstructured":"Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. [Ph.D. Thesis, Committee on Applied Mathematics]."},{"key":"ref_53","unstructured":"Werbos, P.J. (1994). The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, John Wiley & Sons."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction, John Wiley & Sons Ltd.. [2nd ed.].","DOI":"10.1002\/9780470512517"},{"key":"ref_55","unstructured":"Keller, J.M., Liu, D., and Fogel, D.B. (2016). Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation, Wiley. [1st ed.]."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/16168658.2019.1611030","article-title":"Review on reliable pattern recognition with machine learning techniques","volume":"10","author":"Bhamare","year":"2018","journal-title":"Fuzzy Inf. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s10462-018-9641-3","article-title":"Recent progress in semantic image segmentation","volume":"52","author":"Liu","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jiang, H., Peng, M., Zhong, Y., Xie, H., Hao, Z., Lin, J., Ma, X., and Hu, X. (2022). A survey on deep learning-based change detection from high-resolution remote sensing images. Remote Sens., 14.","DOI":"10.3390\/rs14071552"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Qiu, M., and Qiu, H. (2020, January 25\u201327). Review on image processing based adversarial example defenses in computer vision. Proceedings of the 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Baltimore, MD, USA.","DOI":"10.1109\/BigDataSecurity-HPSC-IDS49724.2020.00027"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1080\/01691864.2017.1365009","article-title":"Deep learning in robotics: A review of recent research","volume":"31","author":"Pierson","year":"2017","journal-title":"Adv. Robot."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, X., and Lv, S. (2019). An overview of end-to-end automatic speech recognition. Symmetry, 11.","DOI":"10.3390\/sym11081018"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"G\u00f6\u00e7eri, E. (2020, January 9\u201312). Impact of deep learning and smartphone technologies in dermatology: Automated diagnosis. Proceedings of the 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France.","DOI":"10.1109\/IPTA50016.2020.9286706"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1061\/(ASCE)GT.1943-5606.0000395","article-title":"Validation and application of empirical liquefaction models","volume":"136","author":"Oommen","year":"2010","journal-title":"J. Geotech. Geoenviron. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"114886","DOI":"10.1016\/j.icarus.2022.114886","article-title":"Machine learning as a tool to classify extra-terrestrial landslides: A dossier from Valles Marineris, Mars","volume":"376","author":"Rajaneesh","year":"2022","journal-title":"Icarus"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Krzanowski, W.J., and Hand, D.J. (2009). ROC Curves for Continuous Data, Chapman & Hall\/CRC. [1st ed.]. Monographs on Statistics and Applied Probability; 111.","DOI":"10.1201\/9781439800225"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.jclinepi.2015.02.010","article-title":"The precision\u2013recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases","volume":"68","author":"Ozenne","year":"2015","journal-title":"J. Clin. Epidemiol."},{"key":"ref_68","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Boyd, K., Eng, K.H., and Page, C.D. (2013). Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals, Springer, Machine Learning and Knowledge Discovery in Databases.","DOI":"10.1007\/978-3-642-40994-3_55"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/0022-2496(75)90001-2","article-title":"The area above the ordinal dominance graph and the area below the receiver operating characteristic graph","volume":"12","author":"Bamber","year":"1975","journal-title":"J. Math. Psychol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Manning, C.D., and Raghavan, P. (2008). Introduction to Information Retrieval, Cambridge University Press Cambridge.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods","volume":"17","author":"Tharwat","year":"2021","journal-title":"Appl. Comput. Inform."},{"key":"ref_73","unstructured":"Berrar, D. (2016). On the noise resilience of ranking measures. Proceedings of the Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, 16\u201321 October 2016, Springer. Proceedings, Part II 23."},{"key":"ref_74","unstructured":"Hartman, J., and Kopi\u010d, D. (October, January 27). Scaling TensorFlow to 300 million predictions per second. Proceedings of the 15th ACM Conference on Recommender Systems, Amsterdam, The Netherlands."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"107573","DOI":"10.1016\/j.apacoust.2020.107573","article-title":"Mini-batch sample selection strategies for deep learning based speech recognition","volume":"171","author":"Dokuz","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_76","unstructured":"Denis, R. (2020). Artificial Intelligence by Example: Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills, Packt Publishing. [2nd ed.]."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hu, J., Feng, X., and Zheng, Y. (2021, January 15\u201317). Number of Epochs of Each Model and Hyperband\u2019s Classification Performance. Proceedings of the 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shanghai, China.","DOI":"10.1109\/AINIT54228.2021.00102"},{"key":"ref_78","unstructured":"Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J. (2019). GPM IMERG Early Precipitation L3 1 Day 0.1 Degree x 0.1 Degree V06."},{"key":"ref_79","unstructured":"Kreyszig, E., Kreyszig, H., and Norminton, E.J. (2011). Advanced Engineering Mathematics, Wiley. [10th ed.]."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Beatty, W. (2018). Decision Support Using Nonparametric Statistics, Springer International Publishing. [1st ed.]. SpringerBriefs in Statistics.","DOI":"10.1007\/978-3-319-68264-8"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kokoska, S., and Zwillinger, D. (2000). CRC Standard Probability and Statistics Tables and Formulae, CRC Press.","DOI":"10.1201\/b16923"},{"key":"ref_82","unstructured":"Conover, W.J. (1971). Practical Nonparametric Statistics, John Wiley & Sons."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:04:45Z","timestamp":1760108685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":82,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132332"],"URL":"https:\/\/doi.org\/10.3390\/rs16132332","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]}}}