River stage forecasting with particle swarm optimization

Author name used in this publication: Kwokwing ChauSeries: Lecture notes in computer science2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

A split-step particle swarm optimization algorithm in river stage forecasting

Author name used in this publication: K. W. Chau2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan

Flooding is a natural disaster which has been occurring annually throughout the whole world. The disaster, such as other natural catastrophe could only be mitigated rather than it being completely solved. Runoff prediction proved to be very vital in pre-flooding management system. In recent years, Artificial Neural Network has been applied in various prediction models of hydrological system. It is proposed to model the rainfall-runoff system of Pahang River in Pekan. Mean rainfall data of 5 hydrological stations are used as the input and water level data as the output. The Artificial Neural Networks are trained with Particle Swarm Optimization. The performances of Artificial Neural Networks were measured with Ackley cost function value. Neural network configuration of 450 number of maximum iteration, 6 number of particles and 1.9 and 2.0 values of Particle Swarm Optimization parameter constant for global best (c1) and Particle Swarm Optimization constant for personal best (c2) respectively shows the highest global best function value. The neural network configuration of 300 number of maximum iteration, 3 numbers of particles and 2.2 value of (c1) and (c2) produces lowest global best function value. The output shows Artificial Neural Network trained by Particle Swarm Optimization can successfully model rainfall-runoff. © 2016 IEEE

Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia

Application of a PSO-based neural network in analysis of outcomes of construction claims

Author name used in this publication: K. W. Chau2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Predicting construction litigation outcome using particle swarm optimization

Author name used in this publication: Kwokwing ChauSeries: Lecture notes in computer science2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Development of a PSO-ANN Model for Rainfall-Runoff Response in Basins, Case Study: Karaj Basin

Successful daily river flow forecasting is necessary in water resources planning and management. A reliable rainfall-runoff model can provide useful information for water resources planning and management. In this study, particle swarm optimization algorithm (PSO) as a metaheuristic approach is employed to train artificial neural network (ANN). The proposed PSO-ANN model is applied to simulate the rainfall runoff process in Karaj River for one and two days ahead. In this regard, different combinations of the input variables including flow and rainfall time series in previous days have been taken under consideration in order to obtain the best model's performances. To evaluate efficiency of the PSO algorithm in training ANNs, separate ANN models are developed using Levenberg-Marquardt (LM) training algorithm and the results are compared with those of the PSO-ANN models. The comparison reveals superiority of the PSO algorithm than the LM algorithm in training the ANN models. The best model for 1 and 2 days ahead runoff forecasting has R2 of 0.88 and 0.78. Results of this study shows that a reliable prediction of runoff in 1 and 2 days ahead can be achieved using PSO-ANN model. Overall, results of this study revealed that an acceptable prediction of the runoff up to two days ahead can be achieved by applying the PSO-ANN model