Daily Streamflow Forecasting Using an Enhanced LSTM Neural Network Model

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Abstract Oil and gas consumption for power generation has caused irreversible damage to humanity. To address the attendant effects of fossil fuel utilization, renewable energy is a good alternative. International organizations give support to countries in their transition to a green energy future. This implies that the use of renewable energy is widely supported. It is therefore recommended to utilize renewable energy as it is environmentally friendly. One such type of renewables is water energy. Water cycle has streamflow $$\left({f_{s}} \right)$$ f s as its central component. Having reliable information about future $$f_{s}$$ f s data is essential in hydrological research, as it can help water managers to plan hydropower generation. To also ensure preparedness and mitigation of floods and drought as well as hydropower production planning and management, precise $$f_{s}$$ f s prediction is considered essential. Several modelling methods have been used lately to forecast $$f_{s}$$ f s , namely: physical methods, data-driven approaches such as shallow artificial neural networks (ANNs), and hybrid techniques. Nevertheless, they may not approximate complex relationships as accurate as deep learning techniques. In this study, an innovative deep learning technique based on long short-term memory (LSTM) neural network adapted with data preprocessing algorithm (DPA) is proposed for seasonal $$f_{s}$$ f s forecasting. Considering recent studies on $$f_{s}$$ f s forecasting, one can avow that researchers have been able to employ lag value predictors for future $$f_{s}$$ f s extrapolation, although deep learning techniques can offer good potentials for $$f_{s}$$ f s prediction with complex physical relationship. However, to the best knowledge of the authors, only very few studies have applied LSTM neural network for streamflow forecasting. In addition, there have been attempts to estimate river $$f_{s}$$ f s in Nigeria using some traditional methods though, but the effect of seasonal variation on $$f_{s}$$ f s forecasting has never been investigated in Nigeria. This is the maiden research in Nigeria that considers seasonal variation in LSTM neural network model-based $$f_{s}$$ f s forecasting. Accordingly, the novelty and key contribution of our state-of-the-art research is the development and implementation of a low-cost intelligent deep learning model based on the LSTM neural network enhanced with DPA for day-ahead $$f_{s}$$ f s forecasting. To further demonstrate the $$f_{s}$$ f s modelling capability of our technique, we have examined the performances of two different baseline approaches namely, the linear regression (LinReg) model and the adaptive linear element neural network (ADALINE-NN) model. The results of $$f_{s}$$

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