{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:58:15Z","timestamp":1774965495068,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabi","award":["RSP2024R432"],"award-info":[{"award-number":["RSP2024R432"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms\u2014Na\u00efve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)\u2014alongside land-use\/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries.<\/jats:p>","DOI":"10.3390\/ijgi13120436","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:04:04Z","timestamp":1733198644000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8257-2245","authenticated-orcid":false,"given":"Subbarayan","family":"Sathiyamurthi","sequence":"first","affiliation":[{"name":"Horticultural Research Station, Tamil Nadu Agricultural University, Thadiyankudisai 624212, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4085-1195","authenticated-orcid":false,"given":"Saravanan","family":"Subbarayan","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Institute of Technology (NITT), Tiruchirappalli 620015, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhappan","family":"Ramya","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Agriculture Chemistry, Faculty of Agriculture, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5626-9542","authenticated-orcid":false,"given":"Murugan","family":"Sivasakthi","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Agriculture Chemistry, Faculty of Agriculture, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rengasamy","family":"Gobi","sequence":"additional","affiliation":[{"name":"Agricultural College and Research Institute, Karur 639001, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saleh","family":"Qaysi","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1894-1178","authenticated-orcid":false,"given":"Sivakumar","family":"Praveen Kumar","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Agriculture Chemistry, Faculty of Agriculture, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9339-666X","authenticated-orcid":false,"given":"Jinwook","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Construction Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7802-5070","authenticated-orcid":false,"given":"Nassir","family":"Alarifi","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5569-3727","authenticated-orcid":false,"given":"Mohamed","family":"Wahba","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5939-732X","authenticated-orcid":false,"given":"Youssef","family":"M. Youssef","sequence":"additional","affiliation":[{"name":"Geological and Geophysical Engineering Department, Faculty of Petroleum and Mining Engineering, Suez University, Suez 43518, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1038\/s41598-023-29378-2","article-title":"Increased probability of hot and dry weather extremes during the growing season threatens global crop yields","volume":"13","author":"Heino","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gopi, P., and Karthikeyan, M. (2023, January 2\u20134). Intelligent Crop Recommendation with Yield Prediction using Dragonfly Algorithm based Deep Learning Model. Proceedings of the 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India.","DOI":"10.1109\/ICAIS56108.2023.10073744"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Arafa, N.A., El-Said Salem, Z., Abdeldayem, A.L., Ghorab, M.A., Moustafa, Y.M., Soliman, S.A., Farag, M.H., Purohit, S., Elhag, M., and Youssef, Y.M. (2024). Advancing Deltaic Aquifer Vulnerability Mapping to Seawater Intrusion and Human Impacts in Eastern Nile Delta: Insights from Machine Learning and Hydrochemical Perspective. Earth Syst. Environ., 1\u201326.","DOI":"10.1007\/s41748-024-00518-6"},{"key":"ref_4","first-page":"100466","article-title":"Assessment of crop suitability analysis using AHP-TOPSIS and geospatial techniques: A case study of Krishnagiri District, India","volume":"24","author":"Sathiyamurthi","year":"2024","journal-title":"Environ. Sustain. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s12571-015-0478-1","article-title":"Environmental impacts and constraints associated with the production of major food crops in Sub-Saharan Africa and South Asia","volume":"7","author":"Reynolds","year":"2015","journal-title":"Food Secur."},{"key":"ref_6","unstructured":"FAO (2022). The State of Food Security and Nutrition in the World 2022, FAO."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bhanumathi, S., Vineeth, M., and Rohit, N. (2019, January 4\u20136). Crop Yield Prediction and Efficient use of Fertilizers. Proceedings of the 2019 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2019.8698087"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100217","DOI":"10.1016\/j.dajour.2023.100217","article-title":"A Delphi fuzzy analytic hierarchy process framework for criteria classification and prioritization in food supply chains under uncertainty","volume":"7","author":"Gupta","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s12571-009-0026-y","article-title":"Declining global per capita agricultural production and warming oceans threaten food security","volume":"1","author":"Funk","year":"2009","journal-title":"Food Secur."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11081","DOI":"10.1073\/pnas.0708196105","article-title":"Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development","volume":"105","author":"Funk","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e18512","DOI":"10.1016\/j.heliyon.2023.e18512","article-title":"Salinity hazard drives the alteration of occupation, land use and ecosystem service in the coastal areas: Evidence from the south-western coastal region of Bangladesh","volume":"9","author":"Islam","year":"2023","journal-title":"Heliyon"},{"key":"ref_12","first-page":"57","article-title":"Distribution of soil organic matter in the coastal region of Syria: A case study","volume":"1","author":"Ghanem","year":"2020","journal-title":"DYSONA-Appl. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tavakoli, M., Motlagh, Z.K., Sayadi, M.H., Ibraheem, I.M., and Youssef, Y.M. (2024). Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran. Water, 16.","DOI":"10.3390\/w16192748"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.compag.2012.02.003","article-title":"Integration of MultiCriteria Decision Analysis in GIS to develop land suitability for agriculture: Application to durum wheat cultivation in the region of Mleta in Algeria","volume":"83","author":"Mendas","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jenvman.2014.06.020","article-title":"Land-use suitability analysis for urban development in Beijing","volume":"145","author":"Liu","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.scitotenv.2017.12.035","article-title":"A dynamic viticultural zoning to explore the resilience of terroir concept under climate change","volume":"624","author":"Bonfante","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2294540","DOI":"10.1080\/23311932.2023.2294540","article-title":"Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques","volume":"10","author":"Hussain","year":"2024","journal-title":"Cogent Food Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100018","DOI":"10.1016\/j.srs.2021.100018","article-title":"An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping","volume":"3","author":"Song","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100543","DOI":"10.1016\/j.envc.2022.100543","article-title":"Climate change and Indian agriculture: A systematic review of farmers\u2019 perception, adaptation, and transformation","volume":"8","author":"Datta","year":"2022","journal-title":"Environ. Chall."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"441","DOI":"10.5267\/j.dsl.2015.1.005","article-title":"Synergy of fuzzy AHP and Six Sigma for capacity waste management in Indian automotive industry","volume":"4","author":"Rathi","year":"2015","journal-title":"Decis. Sci. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s13198-022-01705-2","article-title":"Integrated GIS and AHP techniques for land suitability assessment of cotton crop in Perambalur District, South India","volume":"15","author":"Sathiyamurthi","year":"2024","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_22","unstructured":"Ahamed, T. (2024). A Damage-Based Crop Insurance System for Flash Flooding: A Satellite Remote Sensing and Econometric Approach. Remote Sensing Application II. New Frontiers in Regional Science: Asian Perspectives, Springer."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.apgeog.2006.09.004","article-title":"Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt","volume":"27","author":"Shalaby","year":"2007","journal-title":"Appl. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Khalil, M.M.E., Khodary, S.M., Youssef, Y.M., Alsubaie, M.S., and Sallam, A. (2022). Geo-Environmental Hazard Assessment of Archaeological Sites and Archaeological Domes\u2014Fatimid Tombs\u2014Aswan, Egypt. Buildings, 12.","DOI":"10.3390\/buildings12122175"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.jclepro.2019.02.040","article-title":"Managing the water-climate- food nexus for sustainable development in Turkmenistan","volume":"220","author":"Duan","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1080\/10106049.2019.1648564","article-title":"A spatial assessment of land suitability for maize farming in Kenya","volume":"36","author":"Wanyama","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e25112","DOI":"10.1016\/j.heliyon.2024.e25112","article-title":"Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables","volume":"10","author":"Dey","year":"2024","journal-title":"Heliyon"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"126285","DOI":"10.1016\/j.jclepro.2021.126285","article-title":"Will reaching the maximum achievable yield potential meet future global food demand?","volume":"294","author":"Tian","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102453","DOI":"10.1016\/j.asej.2023.102453","article-title":"Scrutinizing the performance of GIS-based analytical Hierarchical process approach and frequency ratio model in flood prediction\u2014Case study of Kakegawa, Japan","volume":"15","author":"Elsadek","year":"2023","journal-title":"Ain Shams Eng. J."},{"key":"ref_31","unstructured":"Wahba, M., Hassan, H.S., Elsadek, W.M., Kanae, S., and Sharaan, M. (2024, August 25). Prediction of Flood Susceptibility Using Frequency Ratio Method: A Case Study of Fifth District, Egypt. Available online: https:\/\/www.iche2022.org\/_files\/ugd\/21d103_be56d464954b4310a2461ccf02fd83b9.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yin, S., Li, J., Liang, J., Jia, K., Yang, Z., and Wang, Y. (2020). Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences. Sustainability, 12.","DOI":"10.3390\/su122310134"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Agrawal, N., Govil, H., and Kumar, T. (2024). Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem. Environ. Dev. Sustain., 1\u201338.","DOI":"10.1007\/s10668-023-04440-1"},{"key":"ref_35","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.landusepol.2015.05.013","article-title":"Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy","volume":"48","author":"Romano","year":"2015","journal-title":"Land Use Policy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compag.2013.07.006","article-title":"Agricultural land use suitability analysis using GIS and AHP technique","volume":"97","author":"Turgut","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109837","DOI":"10.1016\/j.ecolind.2022.109837","article-title":"Agricultural land suitability analysis for an integrated rice\u2013crayfish culture using a fuzzy AHP and GIS in central China","volume":"148","author":"Xue","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104872","DOI":"10.1016\/j.compag.2019.104872","article-title":"Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations","volume":"164","author":"Ransom","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e00265","DOI":"10.1016\/j.geodrs.2020.e00265","article-title":"Digital soil mapping and GlobalSoilMap. Main advances and ways forward","volume":"21","author":"Arrouays","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1016\/j.jhydrol.2015.06.008","article-title":"Flood hazard risk assessment model based on random forest","volume":"527","author":"Wang","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., and Scholten, T. (2020). Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy, 10.","DOI":"10.3390\/agronomy10040573"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Roell, Y.E., Beucher, A., M\u00f8ller, P.G., Greve, M.B., and Greve, M.H. (2020). Comparing a Random Forest Based Prediction of Winter Wheat Yield to Historical Yield Potential. Agronomy, 10.","DOI":"10.5194\/egusphere-egu2020-138"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"M\u00f8ller, A.B., Mulder, V.L., Heuvelink, G.B.M., Jacobsen, N.M., and Greve, M.H. (2021). Can We Use Machine Learning for Agricultural Land Suitability Assessment?. Agronomy, 11.","DOI":"10.3390\/agronomy11040703"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"116645","DOI":"10.1016\/j.marpolbul.2024.116645","article-title":"Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques","volume":"205","author":"Wahba","year":"2024","journal-title":"Mar. Pollut. Bull."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s12665-024-11696-x","article-title":"Examination of the efficacy of machine learning approaches in the generation of flood susceptibility maps","volume":"83","author":"Wahba","year":"2024","journal-title":"Environ. Earth Sci."},{"key":"ref_47","first-page":"1","article-title":"Building Information Modeling Integrated with Environmental Flood Hazard to Assess the Building Vulnerability to Flash Floods","volume":"38","author":"Wahba","year":"2024","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_48","first-page":"179","article-title":"Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and Machine learning algorithms in Kaliyaganj C.D. block, India","volume":"2","author":"Sarkar","year":"2021","journal-title":"Artif. Intell. Geosci."},{"key":"ref_49","first-page":"269","article-title":"Land suitability evaluation using traditional and machine learning approaches: A case study in abiek plain, Qazvin province, Iran","volume":"55","author":"Khamoshi","year":"2024","journal-title":"Iran. J. Soil Water Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s41651-023-00156-y","article-title":"A GIS Framework to Demarcate Suitable Lands for Combine Harvesters Using Satellite DEM and Physical Properties of Soil","volume":"7","author":"Rahman","year":"2023","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_51","first-page":"1020","article-title":"Sustaining Indian agriculture\u2013conservation agriculture the way forward","volume":"91","author":"Abrol","year":"2006","journal-title":"Curr. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"341","DOI":"10.5194\/se-7-341-2016","article-title":"Modeling the contributing factors of desertification and evaluating their relationships to the soil degradation process through geomatic techniques","volume":"7","author":"Shoba","year":"2016","journal-title":"Solid Earth"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sahu, N., Das, P., Saini, A., Varun, A., Mallick, S.K., Nayan, R., Aggarwal, S.P., Pani, B., Kesharwani, R., and Kumar, A. (2023). Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India. Sustainability, 15.","DOI":"10.3390\/su151310101"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s12665-018-7381-y","article-title":"Examining the utility of river restoration approaches for flood mitigation and channel stability enhancement: A recent review","volume":"77","author":"Mondal","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1007\/s11269-022-03328-5","article-title":"A Long-term Global Comparison of IMERG and CFSR with Surface Precipitation Stations","volume":"36","author":"Ghimire","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"108452","DOI":"10.1016\/j.dib.2022.108452","article-title":"A raw data article on the physico-chemical properties of soil from six firkas in Dharmapuri district, Tamil Nadu, India","volume":"43","author":"Ramya","year":"2022","journal-title":"Data Brief"},{"key":"ref_57","unstructured":"Jackson, M. (1958). Soil Chemical Analysis, Prentice Hall Inc."},{"key":"ref_58","unstructured":"Piper, C.S. (2019). Soil and Plant Analysis, Scientific Publishers."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/00010694-193401000-00003","article-title":"An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method","volume":"37","author":"Walkley","year":"1934","journal-title":"Soil Sci."},{"key":"ref_60","unstructured":"Olsen, S.R. (1954). Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"446","DOI":"10.2134\/agronj1949.00021962004100090012x","article-title":"Use of the Flame Photometer in Rapid Soil Tests for K and Ca","volume":"41","author":"Stanford","year":"1949","journal-title":"Agron. J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s00180-020-00999-9","article-title":"What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?","volume":"36","author":"Marcot","year":"2021","journal-title":"Comput. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.geomorph.2008.02.011","article-title":"Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China","volume":"101","author":"Yao","year":"2008","journal-title":"Geomorphology"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support Vector Regression. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers [Internet], Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1080\/13658816.2011.642800","article-title":"Combining AHP with GIS in synthetic evaluation of environmental suitability for living in China\u2019s 35 major cities","volume":"26","author":"Xu","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Boehmke, B., and Greenwell, B.M. (2019). Hands-on Machine Learning with R, Chapman and Hall\/CRC.","DOI":"10.1201\/9780367816377"},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1186\/s40064-016-2941-7","article-title":"The distance function effect on k-nearest neighbor classification for medical datasets","volume":"5","author":"Hu","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Data pre-processing. Applied Predictive Modeling, Springer Science Business Media.","DOI":"10.1007\/978-1-4614-6849-3_3"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.foreco.2011.06.039","article-title":"Estimating forest attribute parameters for small areas using nearest neighbors techniques","volume":"272","author":"McRoberts","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","unstructured":"Sharaff, A., and Gupta, H. (2019). Extra-tree classifier with metaheuristics approach for email classification. Advances in Computer Communication and Computational Sciences: Proceedings of IC4S 2018, Springer.","DOI":"10.1007\/978-981-13-6861-5_17"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s12524-012-0221-8","article-title":"Site Suitability Analysis for Urban Development Using GIS Based Multicriteria Evaluation Technique","volume":"41","author":"Kumar","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, H., and Zheng, H. (2013). True Positive Rate. Encyclopedia of Systems Biology, Springer.","DOI":"10.1007\/978-1-4419-9863-7_255"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.still.2014.07.020","article-title":"Spatial variation of soil nutrients on sandy-loam soil","volume":"144","author":"Bogunovic","year":"2014","journal-title":"Soil Tillage Res."},{"key":"ref_79","unstructured":"Wilding, L.P. (December, January 30). Spatial Variability: Its Documentation, Accommodation and Implication to Soil Surveys. Proceedings of the Soil Spatial Variability Proceedings of a Workshop of the ISSS and the SSA, Las Vegas, NV, USA."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Cooksey, R.W. (2020). Descriptive Statistics for Summarising Data. llustrating Statistical Procedures: Finding Meaning in Quantitative Data, Springer.","DOI":"10.1007\/978-981-15-2537-7"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1007\/s42452-021-04367-0","article-title":"Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe","volume":"3","author":"Soropa","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1016\/j.sjbs.2021.12.050","article-title":"Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation","volume":"29","author":"Gunal","year":"2022","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_83","unstructured":"Montgomery, J., Reid, M.D., and Drake, B. (2016, January 10\u201312). Protocols and structures for inference: A RESTful API for machine learning. Proceedings of the Conference on Predictive APIs and Apps, PMLR, Boston, MA, USA."},{"key":"ref_84","first-page":"1287","article-title":"Using genetic learning neural networks for spatial decision making in GIS","volume":"62","author":"Zhou","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"11763","DOI":"10.1007\/s11042-015-2635-0","article-title":"Comparison of random forest, random ferns and support vector machine for eye state classification","volume":"75","author":"Dong","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_86","first-page":"950","article-title":"Crop recommendation system to maximize crop yield using machine learning technique","volume":"4","author":"Rajak","year":"2017","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Xing, W., Zhou, C., Li, J., Wang, W., He, J., Tu, Y., Cao, X., and Zhang, Y. (2022). Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales. Agronomy, 12.","DOI":"10.3390\/agronomy12092010"},{"key":"ref_88","first-page":"23","article-title":"Using parallel random forest classifier in predicting land suitability for crop production","volume":"8","author":"Senagi","year":"2017","journal-title":"J. Agric. Inform."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the KDD \u201916: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.landusepol.2018.07.034","article-title":"Land management in Mexican sugarcane crop fields","volume":"78","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1590\/S0100-204X2002000800013","article-title":"Geoestat\u00edstica na determina\u00e7\u00e3o da variabilidade espacial de caracter\u00edsticas qu\u00edmicas do solo sob diferentes preparos","volume":"37","author":"Vieira","year":"2002","journal-title":"Pesqui. Agropecu. Bras."},{"key":"ref_94","first-page":"25","article-title":"Unveiling soil and groundwater salinity dynamics and its impact on date palm yield in Southern Basrah, Iraq","volume":"5","author":"Alhamd","year":"2024","journal-title":"DYSONA-Appl. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/436\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:45:37Z","timestamp":1760114737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":94,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["ijgi13120436"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13120436","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]}}}