Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network

Water demand prediction is of great importance for regional water resources planning.In this paper,BP neural network is primarily applied to predict the water demand of Liuzhou in 2020 and 2025,and then 100 groups of forecast results are selected for statistically frequency distribution analysis in...

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Main Author: ZHANG Zhibo
Format: Article
Language:zho
Published: Editorial Office of Pearl River 2021-01-01
Series:Renmin Zhujiang
Subjects:
Online Access:http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.014
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author ZHANG Zhibo
author_facet ZHANG Zhibo
author_sort ZHANG Zhibo
collection DOAJ
description Water demand prediction is of great importance for regional water resources planning.In this paper,BP neural network is primarily applied to predict the water demand of Liuzhou in 2020 and 2025,and then 100 groups of forecast results are selected for statistically frequency distribution analysis in the case of the relative error of prediction results being less than 2%.The results show that the prediction results of Liuzhou obey normal distribution in 2020 and mildly positively skewed distribution in 2025.Moreover,combining the prediction results of Liuzhou in 2025 which are converted to normal distribution by SPSS,and the interval estimation of normal distribution,the prediction interval of Liuzhou water demand,i.e.2.147 8~2.162 2 billion m<sup>3</sup> in 2020 and 2.181 3~2.202 3 billion m<sup>3</sup> in 2025 is obtained under the condition of 95% confidence level.The research has certain theoretical and instructional significance for Liuzhou water resources planning in the future.
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institution Kabale University
issn 1001-9235
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spelling doaj-art-f9f98255c6c046e4ad4afb485b26fc822025-01-15T02:27:59ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247646069Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural NetworkZHANG ZhiboWater demand prediction is of great importance for regional water resources planning.In this paper,BP neural network is primarily applied to predict the water demand of Liuzhou in 2020 and 2025,and then 100 groups of forecast results are selected for statistically frequency distribution analysis in the case of the relative error of prediction results being less than 2%.The results show that the prediction results of Liuzhou obey normal distribution in 2020 and mildly positively skewed distribution in 2025.Moreover,combining the prediction results of Liuzhou in 2025 which are converted to normal distribution by SPSS,and the interval estimation of normal distribution,the prediction interval of Liuzhou water demand,i.e.2.147 8~2.162 2 billion m<sup>3</sup> in 2020 and 2.181 3~2.202 3 billion m<sup>3</sup> in 2025 is obtained under the condition of 95% confidence level.The research has certain theoretical and instructional significance for Liuzhou water resources planning in the future.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.014BP neural networknormal distributioninterval estimation
spellingShingle ZHANG Zhibo
Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
Renmin Zhujiang
BP neural network
normal distribution
interval estimation
title Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
title_full Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
title_fullStr Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
title_full_unstemmed Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
title_short Prediction Research of Water Demand Probability Interval in Liuzhou City Based on BP Neural Network
title_sort prediction research of water demand probability interval in liuzhou city based on bp neural network
topic BP neural network
normal distribution
interval estimation
url http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.014
work_keys_str_mv AT zhangzhibo predictionresearchofwaterdemandprobabilityintervalinliuzhoucitybasedonbpneuralnetwork