Climatic water balance forecasting with machine learning and deep learning models over Bangladesh

AbstractUnderstanding the impact of input variables on black‐box machine learning and deep learning models is necessary. Therefore, this study proposed SHapley Additive exPlanations (SHAP) values to address the problem of the interpretability of the output of the support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and long short‐term memory (LSTM) models to forecast climatic water balance (CWB) within 1–3 months lead‐time. The current study uses two Koppen–Geiger climate zones over Bangladesh: the humid subtropical climate with dry winter and hot summer (Cwa) and the tropical climate (Af‐Am). Monthly antecedent CWB, potential evapotranspiration (PET), convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic oscillation (NAO) are used as the inputs to the proposed model. SHAP values show that CWB (current month) for both climate zones has the maximum impact on the prediction. The effects of atmospheric variables and ocean–atmospheric teleconnections on CWB forecasts over Bangladesh are low. Forecasting results show that the SVM model shows the best performance for CWB forecasts in the Cwa and Af‐Am climate zones in terms of antecedent CWB as input. And this model effectively decreases the negative effect of increasing forecast lead‐time. Since the proposed model can predict CWB in 3‐months lead‐time, it would help policy‐makers and practitioners to reduce the drought and flood impacts in the future by adopting better preparedness plans.