Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River

By Kwok-Wing Chau. 2006

322 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/d05129805e6da37e6261339a083b7f077d751077

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, methodology): “PSO has been successfully used to train ANNs, for instance ANNs to predict river water levels [30], water quality [29] and air pollutant levels [105].”
Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, methodology): “Most of these studies have pointed out that ANNs (as nonlinear models) are simpler, faster to develop and provide more accurate and precise results than physical or numerical-based models.”
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “The ANNs have been shown to give useful results in many fields of hydrology and water resources research (Campolo et al., 2003; Chau, 2006; Muttil and Chau, 2006).”
Prediction of building energy consumption by using artificial neural networks (2009, methodology): “Chau [14,15] proven that ANN can be successfully used in whole area predictions after a learning process and in his another study, he proven the usability of particle swarm optimization training algorithm as an alternative training algorithm for ANN.”
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “PSO has been effectively utilized for solving engineering, classification and optimization problems and fast finding the optimal solutions using two main parameters: position (x) and velocity (V) (Chau 2006; Garcı́a Nieto et al. 2014; Sudheer et al. 2014; Zhang et al. 2014; Kuntoji et al. 2018; Tikh”
Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization (2009, methodology): “PSO and MWARPE have already been applied to the estimation of parameters of hydrologic models [Gill et al., 2006; Chau, 2006], but not in a multivariate context, using only discharge data.”