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Novel reservoir system simulation procedure for gap minimization between water supply and demand

In recent years, with the quick growth of the economy and living standards in Malaysia, keeping up with the water demand is essential for the growth of cultivation, domestic and industrial. With the merits of having dams and reservoirs, water releases from dams are usually used to respond to the wat...

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Main Authors:	Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, El-Shafie, Ahmed
Format:	Article
Published:	Elsevier 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/19960/ https://doi.org/10.1016/j.jclepro.2018.09.237
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spelling	my.um.eprints.199602019-01-11T08:29:49Z http://eprints.um.edu.my/19960/ Novel reservoir system simulation procedure for gap minimization between water supply and demand Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) In recent years, with the quick growth of the economy and living standards in Malaysia, keeping up with the water demand is essential for the growth of cultivation, domestic and industrial. With the merits of having dams and reservoirs, water releases from dams are usually used to respond to the water requirements of downstream dams. To match the practical water requirement considering spatial and temporal conditions, a novel optimization operation model has been formulated for minimizing the gap between the water release from a dam and the water requirement. In this context, there is a need to develop an optimization model to alleviate the complexity and multidimensionality of a dam and reservoir as water supplies and the water demand system. In this research, an optimization algorithm, namely, the shark machine learning algorithm (SMLA) that has high inertia for obtaining its targets, is proposed that mimics the natural shark process. The major objective for the proposed model is attaining the minimum difference between the water demand volume and water release. To examine the proposed model, SMLA has been utilized in determining the optimal operation policies for Timah Tasoh Dam, located in Malaysia. A new procedure to evaluate the performance of optimization models by integrating reservoir inflow forecasting with operational rules generated by optimization models has been proposed. Accordingly, two predictive models, namely, radial basis function neural network (RBF-NN) and support vector regression (SVR), have been developed to forecast monthly reservoir inflow. The test results revealed that the SVR forecasts monthly reservoir inflow better than the RBF-NN model. Additionally, the SMLA attained more reliable, resilient and less vulnerable results in the operation of the reservoir system compared to that of other optimization models. In addition, SMLA has demonstrated a significant change in the performance indicator values when using forecasted reservoir inflow data rather than deterministic reservoir inflow data. Elsevier 2019 Article PeerReviewed Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and El-Shafie, Ahmed (2019) Novel reservoir system simulation procedure for gap minimization between water supply and demand. Journal of Cleaner Production, 206. pp. 928-943. ISSN 0959-6526 https://doi.org/10.1016/j.jclepro.2018.09.237 doi:10.1016/j.jclepro.2018.09.237
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus El-Shafie, Ahmed Novel reservoir system simulation procedure for gap minimization between water supply and demand
description	In recent years, with the quick growth of the economy and living standards in Malaysia, keeping up with the water demand is essential for the growth of cultivation, domestic and industrial. With the merits of having dams and reservoirs, water releases from dams are usually used to respond to the water requirements of downstream dams. To match the practical water requirement considering spatial and temporal conditions, a novel optimization operation model has been formulated for minimizing the gap between the water release from a dam and the water requirement. In this context, there is a need to develop an optimization model to alleviate the complexity and multidimensionality of a dam and reservoir as water supplies and the water demand system. In this research, an optimization algorithm, namely, the shark machine learning algorithm (SMLA) that has high inertia for obtaining its targets, is proposed that mimics the natural shark process. The major objective for the proposed model is attaining the minimum difference between the water demand volume and water release. To examine the proposed model, SMLA has been utilized in determining the optimal operation policies for Timah Tasoh Dam, located in Malaysia. A new procedure to evaluate the performance of optimization models by integrating reservoir inflow forecasting with operational rules generated by optimization models has been proposed. Accordingly, two predictive models, namely, radial basis function neural network (RBF-NN) and support vector regression (SVR), have been developed to forecast monthly reservoir inflow. The test results revealed that the SVR forecasts monthly reservoir inflow better than the RBF-NN model. Additionally, the SMLA attained more reliable, resilient and less vulnerable results in the operation of the reservoir system compared to that of other optimization models. In addition, SMLA has demonstrated a significant change in the performance indicator values when using forecasted reservoir inflow data rather than deterministic reservoir inflow data.
format	Article
author	Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus El-Shafie, Ahmed
author_facet	Allawi, Mohammed Falah Jaafar, Othman Mohamad Hamzah, Firdaus El-Shafie, Ahmed
author_sort	Allawi, Mohammed Falah
title	Novel reservoir system simulation procedure for gap minimization between water supply and demand
title_short	Novel reservoir system simulation procedure for gap minimization between water supply and demand
title_full	Novel reservoir system simulation procedure for gap minimization between water supply and demand
title_fullStr	Novel reservoir system simulation procedure for gap minimization between water supply and demand
title_full_unstemmed	Novel reservoir system simulation procedure for gap minimization between water supply and demand
title_sort	novel reservoir system simulation procedure for gap minimization between water supply and demand
publisher	Elsevier
publishDate	2019
url	http://eprints.um.edu.my/19960/ https://doi.org/10.1016/j.jclepro.2018.09.237
_version_	1643691138174943232
score	13.211869

Novel reservoir system simulation procedure for gap minimization between water supply and demand

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