Water resource management and flood mitigation: hybrid decomposition EMD-ANN model study under climate change

Abstract

The growing population and the rise in urbanization have made managing water a critical concern around the world in recent years. Globally, flooding is one of the most devastating natural disasters. Flood risk mitigation relies heavily on accurate and consistent streamflow forecasts. Pakistan Upper Indus Basin (UIB) is most vulnerable to flooding. Floods have become more frequent in recent decades. UIB can be divided into sub-regions due to its landscape variability, and its collective impact is most prominent in the Massam region. UIB hydrological and meteorological station observations have been used to study seasonal hydro-meteorological variations. To predict flooding, this study proposes a hybrid model combining artificial neural networks as multi-layer perceptron (MLPs) in feed-forward mode, along with empirical mode decomposition (EMD). Data collected by the surface-water hydrology project and Pakistan Meteorological Department from 1960 to 2012, 1969 to 2012, and 1972 to 2012 have been utilized from 17 locations. Statistical parameters and Nash–Sutcliffe Efficiency were measured to analyze the model’s prowess. As a result, decomposition-based models perform better than AI-based models when it comes to prediction accuracy. MLPQTP-EMD performed exceptionally better than competing AI models. The results are further validated by performing a peak value analysis during the flooding season (June–September) achieving a remarkable 91.3% score adding a 5.6% increase by EMD for input data achieving 39.3–32.3% statistical indices scores.