Implementing Time Series Analysis Based Decision Support Systemfor Managing Water Resources

Abstract

The increasing demand for water in light of limited and sometimes non-renewable resources, and the emergence of
new life and industrial patterns, led to a significant escalation in water consumption, as a result of these factors, the quantity of
water resources and the storage of water resources. And forecasting techniques for water imports for the purpose of
determining the appropriate water stock according to the expected imports for the purpose of achieving rational planning and
management for the operation of the dam and the control of water releases. One of the methods used in the first stage is the
time series method, the Box-Jenkins method (ARIMA), which takes into account temporal changes in the study of phenomena.
And analyzing them and identifying the most important properties in building the appropriate model for the phenomenon
studied, secondly, Artificial Neural Networks (ANN) Artificial Neural Networks, which were applied in this research by the
Back Propagation Network. The results of the research in the first stage showed that the Artificial Neural Network (ANN)
method is the best because it has the least sum of squared errors (MSE). Artificial Neural Network (ANN) algorithm and
Support Vector Machine classifier, which were used to classify the output of tank water release. Efficiency through the results
reached by the researcher in order to obtain the highest Accuracy)) to reach the best decision to release water and according to
the need (few, medium, high). And forecasting the time series. This system can be used by the concerned authorities in the
Ministry of Water Resources, as well as the decision-making process by the supporters of the project. A decision to release
water for the purpose of using it for water consumption needs such as (irrigation, agriculture, industry and electricity).