Abstract
Maximum entropy (MaxEnt) is a widely used general-purpose machine learning approach for species distribution modeling. However, in recent years, other researchers have used MaxEnt in other areas such as disease risk mapping, flooding risk assessment, and fire hazard analysis among others. This study demonstrates the use of MaxEnt to map groundwater potential in the province of Marinduque, the Philippines using groundwater wells location and different environmental variables. These environmental variables include elevation, slope, topographic wetness index, drainage density, distance from faults, distance from rivers, rainfall during the wettest month, and annual rainfall. Based on the results, elevation and annual rainfall were the variables with the highest contribution in predicting the groundwater potential in the province.
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Salvacion, A.R. (2022). Groundwater Potential Mapping Using Maximum Entropy. In: Kumar, P., Nigam, G.K., Sinha, M.K., Singh, A. (eds) Water Resources Management and Sustainability. Advances in Geographical and Environmental Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-6573-8_13
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