Development of Particle Swarm Optimization Based Rainfall-Runoff Prediction Model for Pahang River, Pekan

Romlay, Muhammad Rabani Mohd; Rashid, Mir Akmam Noor; Toha, Siti Fauziah

doi:10.1109/iccce.2016.72

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Camera based wayfinding techniques include SLAM [36], Microsoft Kinect [37]- [39], novel liquid sound navigation [40] and FL2-0352 camera [41]. Machine learning methods which are getting numerous attention for researchers nowadays [42], [43] such as particle swarm optimization (PSO) [44] have also been implemented for navigation purposes. [16], [27], [36], [40], [45] [24], [ 25], [27], [46] [30], [32], [33]…”

Section: Discussion On Navigation Techniquesmentioning

confidence: 99%

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

et al. 2021

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Technological advancements have widely contributed to navigation aids. However, their large-scale adaptation for navigation solutions for visually impaired people haven’t been realized yet. Less participation of the visually impaired subject produces a designer-oriented navigation system which overshadows consumer necessity. The outcome results in trust and safety issues, hindering the navigation aids from really contribute to the safety of the targeted end user. This study categorizes electronic travel aids (ETAs) based on experimental evaluations, highlights the designer-centred development of navigation aids with insufficient participation of the visual impaired community. First the research breaks down the methodologies to achieve navigation, followed by categorization of the test and experimentation done to evaluate the systems and ranks it by maturity order. From 70 selected research articles, 51.4% accounts for simulation evaluation, 24.3% involve blindfolded-sighted humans, 22.9% involve visually impaired people and only 1.4% makes it into production and commercialization. Our systematic review offers a bird’s eye view on ETA development and evaluation and contributes to construction of navigational aids which really impact the target group of visually impaired people.

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Section: Discussion On Navigation Techniquesmentioning

confidence: 99%

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

et al. 2021

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“…A funnel was shaped by the optimization technique. Therefore, it was concluded that overflow and drainage of water model can be investigated by applying PSO with huge data size in very less processing time [36]. Early identification can be considered as the most important factor for the breast cancer affected woman.…”

Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

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“…The foundation of Particle Swarm Optimization (PSO) can be represented as a bunch of random particles (solutions) which have their own random position and velocities. With the particles flowing over the hyperspace searching for possible solutions, a search for optima over a series of iteration was completed [47]. The particles learning process is based on its own experience and other particle's prior experience.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Rainfall-Runoff Model Based on ANN with LM, BR and PSO as Learning Algorithms

Romlay¹,

Rashid²,

Toha³

et al. 2019

IJRTE

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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 rainfall-runoff 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.

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Development of Particle Swarm Optimization Based Rainfall-Runoff Prediction Model for Pahang River, Pekan

Cited by 9 publications

References 19 publications

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

Rainfall-Runoff Model Based on ANN with LM, BR and PSO as Learning Algorithms

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