A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events

Young, Chih-Chieh; Liu, Wen‐Cheng; Wu, Ming‐Chang

doi:10.1016/j.asoc.2016.12.052

Cited by 128 publications

(51 citation statements)

References 42 publications

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“…The evaluation results showed a high success rate for the ensemble model. The results proved the efficiency, accuracy, and speed of the model in the susceptibility assessment of floods.Young, Liu, and Wu[179] developed a hybrid physical model through integrating the HEC-HMS model with SVM and ANN for accurate rainfall-runoff modeling during a typhoon. The hybrid models of HEC-HMS-SVR and HEC-HMS-ANN had acceptable capability for hourly prediction.…”

mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

969

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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mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

969

469

View full text Add to dashboard Cite

show abstract

“…The results proved the efficiency, accuracy, and speed in the susceptibility assessment of floods. Young, Liu and Wu [156] developed a hybrid physical model through integrating HEC-HMS model with SVM and ANN for accurate rainfall-runoff modeling during typhoon. The hybrid models of HEC-HMS-SVR and HEC-HMS-ANN have acceptable capability for hourly prediction.…”

Section: Svm-fr Hec-hms-ann Sas-mp Som-r-narx Wavelet-based Narxmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Kwok-wing³

2018

Preprint

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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 has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing 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 have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the 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 the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…Benchmarking studies have shown that the SVM performs the best among current classification techniques [15]. Numerous experiments have shown that Support Vector Machine has satisfactory classification accuracies under a limited number of training samples, and it has been widely used in classification and prediction [9][10][11][12]16,17]. However, there is still a problem that the proper selection of kernel function and its parameters has great influence on the final prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Sehad [11] proposed a novel SVM-based technique to improve rainfall estimation using the multispectral Meteosat Second Generation (MSG) and Spinning Enhanced Visible and Infra Red Imager (SEVIRI) imagery. Younga [12] proposed a new hybrid approach integrating the physically based (Hydrologic modeling system, HEC-HMS) and data-driven (Support vector regression, SVR) models to predict the hourly runoff discharges in the Chishan Creek basin, southern Taiwan. Take SVM for accurate rainfall-runoff modeling.…”

Section: Introductionmentioning

confidence: 99%

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

Jinglin

Liu

et al. 2017

Algorithms

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Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.

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A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events

Cited by 128 publications

References 42 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning, Literature Review

A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms

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