Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index

Abstract

Quantification and prediction of drought events are important for planning and management of water resources in coping with climate change scenarios at global and local scales. In this study, heuristic approaches including Co-Active Neuro Fuzzy Inference System (CANFIS), Multi-Layer Perceptron Neural Network (MLPNN) and Multiple Linear Regression (MLR) were utilized to predict the hydrological drought based on multi-scalar Streamflow Drought Index (SDI) at Naula and Kedar stations located in upper Ramganga River basin, Uttarakhand State, India. The SDI was calculated on 1-, 3-, 6-, 9-, 12- and 24-month time scales (SDI-1, SDI-3, SDI-6, SDI-9, SDI-12, and SDI-24) using monthly streamflow data of 33 years (1975-2007). The significant input variables (lags) for CANFIS, MLPNN, and MLR models were derived using autocorrelation and partial autocorrelation functions (ACF P while MLPNN was the best at 1- and 9-month time scales for Naula station, and at 6- and 9-month time scales for Kedar station. The MLR model was found to be the best at 24-month time scale for Naula station only. The results of this study could be helpful in prediction of hydrological drought on multiple time scales and decision making for remedial schemes to cope with hydrological drought at Naula and Kedar stations.