Showmitra Kumar Sarkar, Rhyme Rubayet Rudra, Swapan Talukdar, Palash Chandra Das, Md Sadmin Nur, Edris Alam, Md Kamrul Islam, Abu Reza Md Towfiqul Islam
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones...
May 6, 2024: Scientific Reports