The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ramli Adnan - One of the best experts on this subject based on the ideXlab platform.
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Hidden neuron variation in multi-layer perceptron for Flood Water level prediction at Kusial station
2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), 2016Co-Authors: Khairah Jaafar, Ramli Adnan, Nurlaila Ismail, Mazidah Tajjudin, Mohd Hezri Fazalul RahimanAbstract:In Malaysia, east coast of peninsular is experiencing the rainy season between mid - October until March every year. Heavy seasonal rains cause the Kelantan River to overflow and Flood the surroundings area. In this paper, the application of feed forward multi-layer perceptron (FFMLP) in neural networks for Flood Water level prediction is presented. The method focused on the neuron variation in hidden layer. By using measured data of three stations; Tualang, Kuala Krai and Kusial, FFMLP neural networks was developed. The inputs are Water level river at three stations and output is Water level river at Kusial station. The numbers of neuron in hidden layer were varied from one to ten and Levenberg Marquadt algorithm is used to train the network. The performance of network was evaluated using Mean Square Error (MSE). It is shown that three neurons in hidden layer afforded the lowest MSE, 0.043. The Regression, R for training network is closed to 1 (0.991), supports that the model is acceptable and able in predicting Water level at Kusial station.
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Flood Water level modeling and prediction using NARX neural network: Case study at Kelang river
2014 IEEE 10th International Colloquium on Signal Processing and its Applications, 2014Co-Authors: Fazlina Ahmat Ruslan, Zainazlan Md Zain, Abd Samad, Ramli AdnanAbstract:Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable Flood prediction is very much needed in order to reduce the effects of Flood disaster. Hence, an accurate Flood Water level prediction is an important task to achieve. Since Flood Water level fluctuation is highly nonlinear, it is very difficult to predict the Flood Water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes Flood Water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of Flood Water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the Flood Water level 10 hours ahead of time.
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Flood prediction using narx neural network and ekf prediction technique a comparative study
International Conference on System Engineering and Technology, 2013Co-Authors: Fazlina Ahmat Ruslan, Abd Manan Samad, Zainazlan Md Zain, Ramli AdnanAbstract:Accurate and reliable Flood Water level prediction is very difficult to achieve as it is often characterized as chaotic in nature. Prediction using conventional neural network techniques with back propagation algorithm which was widely used does not provide reliable prediction results. Flood Water level is characterizing as a dynamic nonlinear properties that cannot be represented by static neural network such as back propagation algorithm. Therefore, NARX NN is propose as the identification model because it could reflect the dynamic characteristics of the Flood Water level, as NARX structure includes the feedback of the network output. This paper compares the prediction performances of NARX model and EKF prediction technique in Flood Water level prediction. EKF is well known as the best nonlinear state estimator. Results showed that NARX model performed better than EKF prediction technique.
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Artificial neural network modelling and Flood Water level prediction using extended Kalman filter
2012 IEEE International Conference on Control System Computing and Engineering, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Accurate Flood Water level prediction are essential for reliable Flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for Flood Water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of Flood Water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the Flood location and three upstream locations of river Water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.
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Flood Water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Flood Water level prediction has long been the earliest forecasting problems that have attracted the interest of many researchers. Accurate prediction of Flood Water level is extremely importance as an early warning system to the public to inform them about the possible incoming Flood disaster. Using the collected data at the upstream and downstream station of a river, this paper proposes a modelling of Flood Water level at downstream station using back propagation neural network (BPN). In order to improve the prediction values, an extended Kalman filter was introduced at the output of the BPN. The introduction of extended Kalman filter at the output of BPN shows significant improvement to the prediction and tracking performance of the actual Flood Water level.
Guy Schumann - One of the best experts on this subject based on the ideXlab platform.
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automatic near real time selection of Flood Water levels from high resolution synthetic aperture radar images for assimilation into hydraulic models a case study
Remote Sensing of Environment, 2012Co-Authors: D C Mason, Guy Schumann, Jeffrey C Neal, Javier Garciapintado, Paul D BatesAbstract:Abstract Flood extents caused by fluvial Floods in urban and rural areas may be predicted by hydraulic models. Assimilation may be used to correct the model state and improve the estimates of the model parameters or external forcing. One common observation assimilated is the Water level at various points along the modelled reach. Distributed Water levels may be estimated indirectly along the Flood extents in Synthetic Aperture Radar (SAR) images by intersecting the extents with the Floodplain topography. It is necessary to select a subset of levels for assimilation because adjacent levels along the Flood extent will be strongly correlated. A method for selecting such a subset automatically and in near real-time is described, which would allow the SAR Water levels to be used in a forecasting model. The method first selects candidate Waterline points in Flooded rural areas having low slope. The Waterline levels and positions are corrected for the effects of double reflections between the Water surface and emergent vegetation at the Flood edge. Waterline points are also selected in Flooded urban areas away from radar shadow and layover caused by buildings, with levels similar to those in adjacent rural areas. The resulting points are thinned to reduce spatial autocorrelation using a top-down clustering approach. The method was developed using a TerraSAR-X image from a particular case study involving urban and rural Flooding. The Waterline points extracted proved to be spatially uncorrelated, with levels reasonably similar to those determined manually from aerial photographs, and in good agreement with those of nearby gauges.
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high resolution 3 d Flood information from radar imagery for Flood hazard management
IEEE Transactions on Geoscience and Remote Sensing, 2007Co-Authors: Guy Schumann, Renaud Hostache, Christian Puech, Lucien Hoffmann, Patrick Matgen, Florian Pappenberger, Laurent PfisterAbstract:This paper presents a remote-sensing-based steady-state Flood inundation model to improve preventive Flood-management strategies and Flood disaster management. The Regression and Elevation-based Flood Information eXtraction (REFIX) model is based on regression analysis and uses a remotely sensed Flood extent and a high-resolution Floodplain digital elevation model to compute Flood depths for a given Flood event. The root mean squared error of the REFIX, compared to ground-surveyed high Water marks, is 18 cm for the January 2003 Flood event on the River Alzette Floodplain (G.D. of Luxembourg), on which the model is developed. Applying the same methodology on a reach of the River Mosel, France, shows that for some more complex river configurations (in this case, a meandering river reach that contains a number of hydraulic structures), piecewise regression is required to yield more accurate Flood Water-line estimations. A comparison with a simulation from the Hydrologic Engineering Centers River Analysis System hydraulic Flood model, calibrated on the same events, shows that, for both events, the REFIX model approximates the Water line reliably
Zainazlan Md Zain - One of the best experts on this subject based on the ideXlab platform.
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Flood Water level modeling and prediction using NARX neural network: Case study at Kelang river
2014 IEEE 10th International Colloquium on Signal Processing and its Applications, 2014Co-Authors: Fazlina Ahmat Ruslan, Zainazlan Md Zain, Abd Samad, Ramli AdnanAbstract:Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable Flood prediction is very much needed in order to reduce the effects of Flood disaster. Hence, an accurate Flood Water level prediction is an important task to achieve. Since Flood Water level fluctuation is highly nonlinear, it is very difficult to predict the Flood Water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes Flood Water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of Flood Water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the Flood Water level 10 hours ahead of time.
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Flood prediction using narx neural network and ekf prediction technique a comparative study
International Conference on System Engineering and Technology, 2013Co-Authors: Fazlina Ahmat Ruslan, Abd Manan Samad, Zainazlan Md Zain, Ramli AdnanAbstract:Accurate and reliable Flood Water level prediction is very difficult to achieve as it is often characterized as chaotic in nature. Prediction using conventional neural network techniques with back propagation algorithm which was widely used does not provide reliable prediction results. Flood Water level is characterizing as a dynamic nonlinear properties that cannot be represented by static neural network such as back propagation algorithm. Therefore, NARX NN is propose as the identification model because it could reflect the dynamic characteristics of the Flood Water level, as NARX structure includes the feedback of the network output. This paper compares the prediction performances of NARX model and EKF prediction technique in Flood Water level prediction. EKF is well known as the best nonlinear state estimator. Results showed that NARX model performed better than EKF prediction technique.
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Artificial neural network modelling and Flood Water level prediction using extended Kalman filter
2012 IEEE International Conference on Control System Computing and Engineering, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Accurate Flood Water level prediction are essential for reliable Flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for Flood Water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of Flood Water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the Flood location and three upstream locations of river Water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.
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Flood Water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Flood Water level prediction has long been the earliest forecasting problems that have attracted the interest of many researchers. Accurate prediction of Flood Water level is extremely importance as an early warning system to the public to inform them about the possible incoming Flood disaster. Using the collected data at the upstream and downstream station of a river, this paper proposes a modelling of Flood Water level at downstream station using back propagation neural network (BPN). In order to improve the prediction values, an extended Kalman filter was introduced at the output of the BPN. The introduction of extended Kalman filter at the output of BPN shows significant improvement to the prediction and tracking performance of the actual Flood Water level.
Paul D Bates - One of the best experts on this subject based on the ideXlab platform.
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automatic near real time selection of Flood Water levels from high resolution synthetic aperture radar images for assimilation into hydraulic models a case study
Remote Sensing of Environment, 2012Co-Authors: D C Mason, Guy Schumann, Jeffrey C Neal, Javier Garciapintado, Paul D BatesAbstract:Abstract Flood extents caused by fluvial Floods in urban and rural areas may be predicted by hydraulic models. Assimilation may be used to correct the model state and improve the estimates of the model parameters or external forcing. One common observation assimilated is the Water level at various points along the modelled reach. Distributed Water levels may be estimated indirectly along the Flood extents in Synthetic Aperture Radar (SAR) images by intersecting the extents with the Floodplain topography. It is necessary to select a subset of levels for assimilation because adjacent levels along the Flood extent will be strongly correlated. A method for selecting such a subset automatically and in near real-time is described, which would allow the SAR Water levels to be used in a forecasting model. The method first selects candidate Waterline points in Flooded rural areas having low slope. The Waterline levels and positions are corrected for the effects of double reflections between the Water surface and emergent vegetation at the Flood edge. Waterline points are also selected in Flooded urban areas away from radar shadow and layover caused by buildings, with levels similar to those in adjacent rural areas. The resulting points are thinned to reduce spatial autocorrelation using a top-down clustering approach. The method was developed using a TerraSAR-X image from a particular case study involving urban and rural Flooding. The Waterline points extracted proved to be spatially uncorrelated, with levels reasonably similar to those determined manually from aerial photographs, and in good agreement with those of nearby gauges.
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coupled 1d quasi 2d Flood inundation model with unstructured grids
Journal of Hydraulic Engineering, 2010Co-Authors: Soumendra Nath Kuiry, Paul D BatesAbstract:A simplified numerical model for simulation of Floodplain inundation resulting from naturally occurring Floods in rivers is presented. Flow through the river is computed by solving the de Saint Venant equations with a one-dimensional (1D) finite volume approach. Spread of excess Flood Water spilling overbank from the river onto the Floodplains is computed using a storage cell model discretized into an unstructured triangular grid. Flow exchange between the one-dimensional river cells and the adjacent Floodplain cells or that between adjoining Floodplain cells is represented by diffusive-wave approximated equation. A common problem related to the stability of such coupled models is discussed and a solution by way of linearization offered. The accuracy of the computed flow depths by the proposed model is estimated with respect to those predicted by a two-dimensional (2D) finite volume model on hypothetical river-Floodplain domains. Finally, the predicted extent of inundation for a Flood event on a stretch of River Severn, United Kingdom, by the model is compared to those of two proven two-dimensional flow simulation models and with observed imagery of the Flood extents.
Fazlina Ahmat Ruslan - One of the best experts on this subject based on the ideXlab platform.
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Flood Water level modeling and prediction using NARX neural network: Case study at Kelang river
2014 IEEE 10th International Colloquium on Signal Processing and its Applications, 2014Co-Authors: Fazlina Ahmat Ruslan, Zainazlan Md Zain, Abd Samad, Ramli AdnanAbstract:Flood disaster has becomes major threat around the world because it causes loss of lives and damages to property. Thus, reliable Flood prediction is very much needed in order to reduce the effects of Flood disaster. Hence, an accurate Flood Water level prediction is an important task to achieve. Since Flood Water level fluctuation is highly nonlinear, it is very difficult to predict the Flood Water level. Artificial Neural Network is well known technique is solving nonlinear cases and Nonlinear Auto Regressive with Exogenous Input (NARX) model is one class of Artificial Neural Network model. Thus, this paper proposes Flood Water level modeling and prediction using Nonlinear Auto Regressive with Exogenous Input (NARX) model to overcome the nonlinearity problem and come out with an advanced neural network model for the prediction of Flood Water level 10 hours in advance. The input and output parameters used in this model are based on real-time data obtained from Department of Irrigation and Drainage Malaysia. Results showed that NARX model successfully predicted the Flood Water level 10 hours ahead of time.
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Flood prediction using narx neural network and ekf prediction technique a comparative study
International Conference on System Engineering and Technology, 2013Co-Authors: Fazlina Ahmat Ruslan, Abd Manan Samad, Zainazlan Md Zain, Ramli AdnanAbstract:Accurate and reliable Flood Water level prediction is very difficult to achieve as it is often characterized as chaotic in nature. Prediction using conventional neural network techniques with back propagation algorithm which was widely used does not provide reliable prediction results. Flood Water level is characterizing as a dynamic nonlinear properties that cannot be represented by static neural network such as back propagation algorithm. Therefore, NARX NN is propose as the identification model because it could reflect the dynamic characteristics of the Flood Water level, as NARX structure includes the feedback of the network output. This paper compares the prediction performances of NARX model and EKF prediction technique in Flood Water level prediction. EKF is well known as the best nonlinear state estimator. Results showed that NARX model performed better than EKF prediction technique.
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Artificial neural network modelling and Flood Water level prediction using extended Kalman filter
2012 IEEE International Conference on Control System Computing and Engineering, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Accurate Flood Water level prediction are essential for reliable Flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for Flood Water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of Flood Water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the Flood location and three upstream locations of river Water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.
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Flood Water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor
2012 IEEE Control and System Graduate Research Colloquium, 2012Co-Authors: Ramli Adnan, Fazlina Ahmat Ruslan, Abd Samad, Zainazlan Md ZainAbstract:Flood Water level prediction has long been the earliest forecasting problems that have attracted the interest of many researchers. Accurate prediction of Flood Water level is extremely importance as an early warning system to the public to inform them about the possible incoming Flood disaster. Using the collected data at the upstream and downstream station of a river, this paper proposes a modelling of Flood Water level at downstream station using back propagation neural network (BPN). In order to improve the prediction values, an extended Kalman filter was introduced at the output of the BPN. The introduction of extended Kalman filter at the output of BPN shows significant improvement to the prediction and tracking performance of the actual Flood Water level.