Watershed Prioritization and Decision-Making Based on Weighted Sum Analysis, Feature Ranking, and Machine Learning Techniques

Prediction and validation of Compound factors for prioritization of watersheds are an essential application using machine learning (ML) techniques in water resource engineering. The current paper proposes a methodology to derive 14 morphometric and 3 topo-hydrological parameters using remote sensing (RS) and geographical information systems (GIS). Compound factor (CF) values are calculated using weighted sum analysis (WSA), ReliefF, and the Pearson correlation coefficient, and the important parameters are identified. Two machine learning models, multilayer perceptron (MLP) and support vector machine (SVM), are utilized to predict CF values. Predication accuracy of ML models is evaluated with three parameters, mean absolute error (MAE), Pearson correlation coefficient (PCC), and root mean square error (RMSE). It is observed that the maximum value of PCC equal to 1 is achieved through ReliefF and SVM, whereas minimum MAE and RMSE are observed with ReliefF and SVM when Tenfold cross-validation is applied. Since ReliefF shows better results, CF values are calculated and applied to create the watershed. The proposed methodology is helpful for accurately predicting CF values and advantageous to allocating the proper watershed, which will be useful for decision-making and implementation of conservation techniques for soil and water.