scholarly journals Prediction and Analysis of Water Resources using Machine Learning Algorithm

Water demand prediction plays an important role in urban and environmental planning, ecological development, decision-making processes and optimum utilization of water resources. A precise water demand prediction has a key job in the forecasting, design, process, and organisation of water resources frameworks. The under stress natural resources and the ever increasing population size makes it dominant to accurately and efficiently forecast water demand in the urban area which is possible by applying data mining techniques on the huge volumes of available water data. This paper focuses on building precise predictive models for water demand prediction using support vector machine which takes care of the nonlinear changeability of water demand at diverse levels for optimal operations

2012 ◽  
Vol 599 ◽  
pp. 701-704
Author(s):  
Zhen Quan Tang ◽  
Gang Liu ◽  
Wen Nian Xu ◽  
Zhen Yao Xia ◽  
Hai Xiao

Prediction of water demand is a basic link in water resources plan and management. Reasonable and accurate prediction of storage helps to develop the plan of water resources the next year, which is very favorable to improve the utilization ratio of water resources and reduce the waste of water resources. This paper uses BP neural network to simulate and predict the water content based on the data of water in recent ten years in Hubei province and evaluates the forecast results. The results show that BP neural network for water demand prediction is feasible.


2012 ◽  
Vol 591-593 ◽  
pp. 1320-1324 ◽  
Author(s):  
Xiang Hong Xue ◽  
Xiao Feng Xue ◽  
Lei Xu

construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this model is higher than SVM, BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.


2017 ◽  
Vol 32 (2) ◽  
pp. 401-416 ◽  
Author(s):  
Yanhu He ◽  
Jie Yang ◽  
Xiaohong Chen ◽  
Kairong Lin ◽  
Yanhui Zheng ◽  
...  

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