Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region

Abstract

Reservoir inflow (Qflow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Qflow at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm “genetic algorithm (GA)”, for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.