Wan Ishak W.H.Ku-Mahamud K.R.Md Norwawi N.2024-05-292024-05-29201197836400000001865092910.1007/978-3-642-22170-5_322-s2.0-79960354050https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960354050&doi=10.1007%2f978-3-642-22170-5_32&partnerID=40&md5=2c3c96d7b6e040c5708578bd7abefbcfhttps://oarep.usim.edu.my/handle/123456789/9564Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good. � 2011 Springer-Verlag.en-USEmergency ManagementForecastingIntelligent Decision Support SystemNeural NetworkDecision modelsEmergency managementFlood risksForecasting modelsHeavy rainfallIntelligent decision supportIntelligent decision support systemsReservoir operationReservoir waterReservoir water levelSituation assessmentTemporal data miningWater releaseComputer simulationData miningDecision makingDecision support systemsForecastingHeavy waterModelsNeural networksRainReservoir managementRisk managementSoftware engineeringWater levelsReservoirs (water)Conference Paper365379179 CCISPART 1