Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model

Panda, Rutuparna; Pramanik, Niranjan; Bala, Biplab

doi:10.1016/j.cageo.2009.07.012

Cited by 99 publications

(43 citation statements)

References 16 publications

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“…management. Even with the recent improvements in numerical weather prediction (NWP) models, artificial intelligence (AI) methods, and ML, short-term prediction remains a challenging task [147][148][149][150][151][152]. This section is divided into two subsections-single and hybrid methods of ML-to individually investigate each group of methods.…”

Section: Nomentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

960

469

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

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Section: Nomentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

960

469

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“…The one-dimensional flood routing hydrodynamic model is a physically-based model that can be used to predict water stages response to high freshwater discharge into the river during typhoon events. For the ANN model, which is a data-driven technique, predictability could be increased by providing a large number of appropriate input-output data sets during the training and verification phases [35,55,56]. In this study, we provide an alternative approach: the combination of the one-dimensional flood routing hydrodynamic model (i.e., physical-based model) and the ANN model (i.e., black box model) to improve the accuracy of water stage predictions along the river.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Liu

Chung

2014

Water

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Accurate simulations of river stages during typhoon events are critically important for flood control and are necessary for disaster prevention and water resources management in Taiwan. This study applies two artificial neural network (ANN) models, including the back propagation neural network (BPNN) and genetic algorithm neural network (GANN) techniques, to improve predictions from a one-dimensional flood routing hydrodynamic model regarding the water stages during typhoon events in the Danshuei River system in northern Taiwan. The hydrodynamic model is driven by freshwater discharges at the upstream boundary conditions and by the water levels at the downstream boundary condition. The model provides a sound physical basis for simulating water stages along the river. The simulated results of the hydrodynamic model show that the model cannot reproduce the water stages at different stations during typhoon events for the model calibration and verification phases. The BPNN and GANN models can improve the simulated water stages compared with the performance of the hydrodynamic model. The GANN model satisfactorily predicts water stages during the training and verification phases and exhibits the lowest values of mean absolute error, root-mean-square error and peak error compared with the simulated results at different stations using the hydrodynamic model and the BPNN model. Comparison of the simulated results shows that the GANN OPEN ACCESSWater 2014, 6 1643 model can be successfully applied to predict the water stages of the Danshuei River system during typhoon events.

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“…The data-driven ANNs based upon sufficient training with appropriate input-output data sets can accurately predict the runoff discharge in the watershed [22,23] but with the drawback of black-box feature hindering the simulation of physical processes [24]. The multi-model combination approach for hydrological research is promising.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

Young

Liu

Chung

2015

Neural Comput & Applic

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The accurate prediction of hourly runoff discharge in a river basin during typhoon events is of critical importance in operational flood control and management. This study utilizes three model approaches to predict runoff discharge in the Laonong Creek basin in southern Taiwan: the hydrological engineering center hydrological modeling system (HEC-HMS) model and two hybrid models which combine the HEC-HMS model with a genetic algorithm neural network (GANN) and an adaptive neuro-fuzzy inference system approach (ANFIS). Hourly runoff discharge data during seven heavy rainfall/typhoon events were collected for model calibration (training) and validation. Six statistical indicators [i.e., mean absolute error, root-mean-square error, coefficient of correlation, error of time to peak discharge, error of peak discharge, and coefficient of efficiency (CE)] were used to evaluate the prediction accuracy. The simulation results indicate that the HEC-HMS model cannot satisfactorily predict hourly runoff discharge during the typhoon events. Both hybrid approaches that use the HEC-HMS model in conjunction with the GANN and ANFIS models can significantly improve the prediction accuracy for the n-h-ahead runoff discharge.

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Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model

Cited by 99 publications

References 16 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System

Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

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