Assessing competing policies at Ubonratana reservoir, Thailand

Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Soundharajan, B.

doi:10.1680/wama.13.00059

Cited by 15 publications

(11 citation statements)

References 13 publications

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“…For the lower level line, there is only a slight difference. Therefore, the reservoirs can increase storage capacity for use at November and into the next dry season, which is beneficial in reducing the risk of water shortage, as in the other studies (Ashofteh et al., 2015; Chiamsathit et al., 2015).…”

Section: Resultsmentioning

confidence: 92%

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Thongwan

Kangrang

Prasanchum

2019

Heliyon

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Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966–2016 (51 years) including the future inflow during 2017–2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.

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Section: Resultsmentioning

confidence: 92%

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Thongwan

Kangrang

Prasanchum

2019

Heliyon

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“…Further, the new obtained rule curves from CTSA and CGA model were used to evaluate with the future situation of B2 scenario [24]. e future inflows to reservoir were created by SWAT model that considered climate changes [25][26][27][28]. Table 2.…”

Section: Illustrative Applicationmentioning

confidence: 99%

Development of Future Rule Curves for Multipurpose Reservoir Operation Using Conditional Genetic and Tabu Search Algorithms

Kangrang

Prasanchum

Hormwichian

2018

Advances in Civil Engineering

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Optimal rule curves are necessary guidelines in the reservoir operation that have been used to assess performance of any reservoir to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements. is study applied the conditional genetic algorithm (CGA) and the conditional tabu search algorithm (CTSA) technique to connect with the reservoir simulation model in order to search optimal reservoir rule curves. e Ubolrat Reservoir located in the northeast region of ailand was an illustrative application including historic monthly inflow, future inflow generated by the SWAT hydrological model using 50-year future climate data from the PRECIS regional climate model in case of B2 emission scenario by IPCC SRES, water demand, hydrologic data, and physical reservoir data. e future and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating water situation. e situations of water shortage and excess water were shown in terms of frequency magnitude and duration. e results have shown that the optimal rule curves from CGA and CTSA connected with the simulation model can mitigate drought and flood situations than the existing rule curves. e optimal future rule curves were more suitable for future situations than the other rule curves.

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“…The original rule curves were also provided by the EGAT; the improved versions of these (see Fig. 2) developed by Chiamsathit et al (2014) were used in the current study.…”

Section: Study Area and Datamentioning

confidence: 99%

Inflow forecasting using Artificial Neural Networks for reservoir operation

2016

Self Cite

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Abstract. In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

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Assessing competing policies at Ubonratana reservoir, Thailand

Cited by 15 publications

References 13 publications

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Development of Future Rule Curves for Multipurpose Reservoir Operation Using Conditional Genetic and Tabu Search Algorithms

Inflow forecasting using Artificial Neural Networks for reservoir operation

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