Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Ishak, Wan Hussain Wan; Ku-Mahamud, Ku Ruhana; Norwawi, Norita Md

doi:10.1007/978-3-642-22170-5_32

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…For example, during emergencies such as flood and drought seasons, reservoirs act as defense mechanisms to reduce the risk of flooding and to maintain water supply. During this period, decisions regarding water release is critical . Another example is the prediction of water levels at Kainji Dam, which supplies water to Nigeria's largest hydropower generation station in which ANNs were built to generate a more efficient power supply .…”

Section: Discussionmentioning

confidence: 99%

Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation

et al. 2018

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In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to test the ANN-based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King's College Hospital (KCH; n = 822). The ANN was trained and tested using KCH pairs for both 3- and 12-month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule-based system for facilitating the decision about the most appropriate D-R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR-area under the curve [AUC] = 0.94; MS-AUC = 0.94) and 12 months (CCR-AUC = 0.78; MS-AUC = 0.82), almost 15% higher than the best obtained by other known scores such as Model for End-Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR-E database. In conclusion, the use of ANN for D-R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists. Liver Transplantation 24 192-203 2018 AASLD.

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Section: Discussionmentioning

confidence: 99%

Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation

et al. 2018

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“…Conceptual model of the reservoir system (Source: Ishak et al, 2011) Typically, the reservoir water release decision is determined by human experts (Ishak et al, 2015). The decisions are based on their past experience, current reservoir water level, and upstream inflow.…”

Section: Figurementioning

confidence: 99%

“…Reservoirs are used as one of the mitigation methods to reduce the direct effects of flood. In the occurrence of flood, reservoir water release decision is a critical action that takes place in the reservoir operation (Ishak et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Flood Stage of a Reservoir Based on the Changes in Upstream Rainfall Pattern

Mohamad

Ishak

2019

JTOM

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Flood is amongthe major disasters in Malaysia. Flood occurs whenthe existing waterways areunable to support large amountsof waterduring heavy rain seasons. Reservoirshavebeen used as one of the flood mitigation approachesin the country. A reservoir can hold excessive water to ensure water flow tothedownstream area is under the safe capacity of the waterway. However, due to the needs of the society, a reservoir also serves other purposes such as water supply and recreation. Therefore, reservoir water storage should be maintained to satisfy water usage,and at the same time,the water needsto be released to reserve space forincoming water. This conflict causesproblemsto reservoir operatorswhen making the water release decision. In this paper, a forecasting model wasproposed to forecast the flood stage of a reservoir based on the upstream rainfall pattern. This model couldbe used by reservoir operatorsin the early decision-makingstageofreleasingwater before the reservoir reaches its maximum capacity. Simultaneously,the reservoir water level could be maintainedfor other uses. In this study, the experiments conducted provedthat an Artificial Neural Network is capable ofproducingan acceptable performance in terms of its accuracy.

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“…The reservoir can be defined as a natural or artificial lake, or pond where water is collected and stored that are used for multipurpose operation such as water supply, flood control, hydropower generation and other purposes like agricultural and recreation [2]. The reservoir water release decision is one of the challenging tasks for the reservoir operator in order to determine the quantities of water to be stored and to be released from a reservoir [3].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

Mokhtar¹,

Ishak²,

Norwawi³

2016

JOAMS

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Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Cited by 10 publications

References 22 publications

Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation

Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation

Forecasting the Flood Stage of a Reservoir Based on the Changes in Upstream Rainfall Pattern

Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

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