Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor

Adnan, Ramli; Ruslan, Fazlina Ahmat; Samad, Abd Manan; Zain, Zainazlan Md

doi:10.1109/icsgrc.2012.6287127

Cited by 35 publications

(19 citation statements)

References 13 publications

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“…The results indicated using RNN to forecast natural inflows into hydropower reservoirs outperformed the traditional stochastic and conceptual models. Adnan et al [20] predicted the flood water level using Back Propagation Neural Network (BPN) for flood disaster mitigation. BPN with an extended Kalman filter showed significant improvement to the prediction of the flood water level.…”

Section: Grnn [Generalized Regressionmentioning

confidence: 99%

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Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

et al. 2019

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The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.

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Section: Grnn [Generalized Regressionmentioning

confidence: 99%

“…Adnan et al [20] mentioned nonlinear models, such as support vector regression (SVR) outperformed linear models or time series models in the prediction of streamflow.…”

Section: Grnn [Generalized Regressionmentioning

confidence: 99%

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

et al. 2019

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“…Chen et al used an improved genetic algorithm coupling a back propagation neural network (IGA-BPNN) model for water level prediction, and they used the ground station data of the Hanjiang River as the input of the model [1]. Ramli Adnan et al introduced the extended Kalman filter at the output of the BPNN to show the improvement of the prediction of the actual flood level [19], and he also used the data of upstream and downstream stations of the river as the input. Although many people use site data modeling, there are many areas with sparse or even no site records, which makes it hard to apply the data-driven method.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

et al. 2020

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Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

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“…In recent times, it has become possible to take into account all the factors affecting water level fluctuation, due to the development of computer technology for processing big data. ANN is widely used to forecast the groundwater level [32], water level in rivers [33][34][35], reservoirs [36][37][38], and wetlands [39,40]. However, research into water level prediction in wetlands is more recent than other areas of study, with the research focusing on the use of ANN [39,40].…”

Section: Introductionmentioning

confidence: 99%

Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

Choi

Kim

Han

et al. 2019

Water

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Wetlands play a vital role in hydrologic and ecologic communities. Since there are few studies conducted for wetland water level prediction due to the unavailability of data, this study developed a water level prediction model using various machine learning models such as artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM). The Upo wetland, which is the largest inland wetland in South Korea, was selected as the study area. The daily water level gauge data from 2009 to 2015 were used as dependent variables, while the meteorological data and upstream water level gauge data were used as independent variables. Predictive performance evaluation using RF as the final model revealed 0.96 value for correlation coefficient (CC), 0.92 for Nash–Sutcliffe efficiency (NSE), 0.09 for root mean square error (RMSE), and 0.19 for persistence index (PI). The results indicate that the water level of the Upo wetland was well predicted, showing superior results compared to that of the ANN, which was used in a previous study. The results intend to provide basic data for development of a wetland management method, using water levels of previously ungauged areas.

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Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor

Cited by 35 publications

References 13 publications

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

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