Abstract
In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash–Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.
Highlights
Over 150,000 small-to-medium-sized irrigation reservoirs exist in Japan
70% of them were built before modern times, or over 150 years ago, for irrigation purposes, and the oldest recorded pond was built over 1500 years ago
We aimed to develop and assess hydrological models using the deep learning algorithm, Long short-term memory (LSTM) ED, for predicting water levels applicable to agricultural reservoirs
Summary
Over 150,000 small-to-medium-sized irrigation reservoirs exist in Japan. Many of them are small, ranging from several hundred to 100,000 m cubed. The output layer, in turn, uses the results obtained from the first step as the input for the time step By repeating this sequence, a continuous output is made possible using the LSTM unit [23]. The efficiency of LSTM analysis has been shown in a runoff simulation model during snowfalls [30] and management models for reservoir operations [31,32] Development in this field includes “hybrid models” that combine physical theories in hydrology with the LSTM algorithm [14,32] and models that combine several deep learning algorithms [33,34] or statistical techniques [27,35]. We aimed to develop and assess hydrological models using the deep learning algorithm, LSTM ED, for predicting water levels applicable to agricultural reservoirs. The water level, rainfall data, and discharge events from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) were used in our analysis
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