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Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network
Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions. HIGHLIGHTS A water demand forecasting model based on principal component analysis (PCA) and back propagation (BP) neural network is proposed.; PCA is used to reduce the dimensionality of the data to reduce the computational complexity.; Compared with other existing models, the prediction accuracy of the PCA-BP model has been significantly improved.;
Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network
Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions. HIGHLIGHTS A water demand forecasting model based on principal component analysis (PCA) and back propagation (BP) neural network is proposed.; PCA is used to reduce the dimensionality of the data to reduce the computational complexity.; Compared with other existing models, the prediction accuracy of the PCA-BP model has been significantly improved.;
Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network
Junhao Wu (author) / Zhaocai Wang (author) / Leyiping Dong (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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