Watershed Prioritization and Decision Making Based On Weighted Sum Analysis, Feature Ranking and Machine Learning Techniques.

Abstract Prediction and validation of Compound factors for prioritization of watersheds is an essential application using Machine Learning (ML) Techniques in water resources engineering. In the current paper, a method is proposed to derive 14 morphometric and 3 Topo-hydrological parameters using Remote Sensing (RS) and Geographical Information System (GIS), whereas prediction and validation of compound factor using ML techniques. Compound factor (CF) values are calculated using Weighted Sum Analysis (WSA), ReliefF, correlation coefficient techniques. A ten-fold cross-validation technique is applied to two machine learning models Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). Predication accuracy of models has been further achieved by feature ranking. The accuracy of ML models is evaluated with three parameters, Mean Absolute Error (MEA), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). With the ranked features and Ten-fold cross-validation, prediction results were found to be better. The methodology will be useful for the accurate prediction of CF values and to reduce the uncertainty in watershed prioritization for conservation techniques for soil and water.