Search Results - (( water estimation method algorithm ) OR ( based optimization based algorithm ))*
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. …”
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Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm
Published 2023“…Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States…”
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Optimization of operational policies for the Minab Reservoir, Southern Iran
Published 2012“…Through the hedging rule optimization an algorithm was developed to determine the benefit of water release and the water conserved in the reservoir. …”
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Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River
Published 2025“…This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. …”
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Mixed Unscented Kalman Filter and differential evolution for parameter identification
Published 2013“…UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. …”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm
Published 2018“…The results for the Wilson flood showed that the proposed model could reduce the Sum of Squared Deviations (SSD) value by 89%, 51%, 93%, 69%, and 88%, compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Pattern Search (PS) algorithm, Harmony Search (HS) algorithm, and Honey Bee Mating Optimization (HBMO), respectively. …”
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Time series modeling of water level at Sulaiman Station, Klang River, Malaysia
Published 2010“…The estimation of parameters of the model is accomplished using the hybrid learning algorithm consisting of standard neural network backpropagation algorithm and least squares method. …”
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Using genetic algorithms to optimise land use suitability
Published 2012“…The Erosion Potential Method (EPM) used in erosion estimation and sediment yield of the study. …”
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Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique
Published 2017“…This was achieved by solving an optimization problem using the particle swarm optimization (PSO) algorithm in which the optimal yields were determined by estimating the optimal values of the unknown C and nparameters. …”
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An optimized ensemble for predicting reservoir rock properties in petroleum industry
Published 2013“…The first method isbased on fuzzy genetic algorithm to overcome the premature convergence. …”
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Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran
Published 2015“…A new optimization approach was proposed for redesign monitoring network wells using optimization algorithm based on the vulnerability of aquifer to contaminations, estimation error of sampling wells, nearest distance between wells, and source of contamination in the study area. …”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network
Published 2024Subjects:Article -
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Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations
Published 2023“…Decision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorithm; error analysis; estimation method; flood control; modeling; river discharge; river flow; India…”
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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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Automated underwater vision system for detection and classification of marine life using CNN YOLO-based model / Mohamed Syazwan Asyraf Rosli
Published 2022“…The Adaptive Moment Estimation (Adam) optimizer and Learning Rate on Plateau are employed to optimize the model's training regime. …”
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Modelling of home energy management system using heuristic optimization for sustainable energy utilization
Published 2023“…The developed FLC-based PSO provides an optimal controller output for predicting optimal schedules of the HEMS. …”
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Model selection approaches of water quality index data
Published 2016“…In order to select the best model, it is vital to ensure that proper estimation method is chosen in the modelling process.Different estimators have been proposed for the estimation of parameters of a model, including the least square and iterative estimators.This study aims to evaluate the forecasting performances of two algorithms on water quality index (WQI) of a river in Malaysia based on root mean square error (RMSE) and geometric root mean square error (GRMSE).Feasible generalised least squares (FGLS) and iterative maximum likelihood (ML) estimation methods are used in the algorithms, respectively.The results showed that SUREMLE-Autometrics has surpassed SURE-Autometrics; another simultaneous selection procedure of multipleequation models.Two individual selections, namely Autometrics-SUREMLE and Autometrics-SURE, though showed consistency only for GRMSE.All in all, ML estimation is a more appropriate method to be employed in this seemingly unrelated regression equations (SURE) model selection.…”
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Multiple equations model selection algorithm with iterative estimation method
Published 2016“…This estimation method is equivalent to maximum likelihood estimation at convergence. …”
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