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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance

机译：利用人工智能模型预测水库系统水文参数及其对运行性能的影响

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摘要

Obtaining successful operation rules for dam and reservoir systems is crucial for improving water management to meet the increase in agricultural, domestic and industrial activities. Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. The main purpose of an operation rule is to minimize the gap between water supply and water demand patterns. To examine the optimized model performance, the simulation of a dam and reservoir system is usually carried out for a particular period utilizing the generated operation rule. During the simulation procedure, although reservoir inflow and evaporation are stochastic variables that are required to be forecasted during simulation, they are considered deterministic variables. This study attempts to integrate a forecasting model for reservoir inflow and evaporation with the operation rules generated from optimization models during the simulation procedure. The present study employs several optimization models to generate an optimal operation rule and two different forecasting models for reservoir inflow and reservoir evaporation. The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). Two different forecasting models have been developed for reservoir inflow and evaporation using the radial basis function neural network (RBF-NN) and support vector regression (SVR). It is necessary to analyze the proposed simulation procedure for examining the operation rule to comprehend the analysis under different optimal operation rules and levels of accuracy for both hydrological variables. The suggested models have been applied to generate optimal operation policies and reservoir inflow and evaporation forecasts for the Timah Tasoh dam (TTD) located in Malaysia. The results show that the major findings regarding the model performance during the simulation period indicate the necessity to pay attention to evaluating the optimized model performance by considering the results of the forecasting model for both the hydrological variables of reservoir inflow and reservoir evaporation rather than the deterministic values.
机译：获得大坝和水库系统成功的运行规则对于改善水管理以满足农业，家庭和工业活动的增加至关重要。已经进行了数项研究工作，以利用不同的优化算法为大坝和水库系统生成最佳运行规则。操作规则的主要目的是最小化供水和需水模式之间的差距。为了检查优化的模型性能，通常使用生成的操作规则在特定时期内对大坝和水库系统进行仿真。在模拟过程中，尽管储层流入和蒸发是在模拟过程中需要预测的随机变量，但它们被视为确定性变量。本研究试图在模拟过程中将储层流入和蒸发的预测模型与优化模型生成的操作规则相结合。本研究采用了几种优化模型来生成最优运行规则，并采用了两种不同的储层流入和储层蒸发预测模型。本研究中使用的三种不同的优化算法是遗传算法（GA），粒子群优化（PSO）算法和鲨鱼机器学习算法（SMLA）。利用径向基函数神经网络（RBF-NN）和支持向量回归（SVR），已经为储层的流入和蒸发开发了两种不同的预测模型。有必要分析拟议的模拟程序以检查运行规则，以理解不同水文变量的最佳运行规则和精度水平下的分析。建议的模型已用于为马来西亚的Timah Tasoh大坝（TTD）生成最佳运营策略以及储层流入和蒸发预测。结果表明，在模拟期间有关模型性能的主要发现表明，有必要注意考虑优化模型性能的因素，而不是确定性，而要考虑模型预测结果对储层入水量和储层蒸发量的影响。价值观。

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