Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate

Gizaw, Mesgana Seyoum; Gan, Thian Yew

doi:10.1016/j.jhydrol.2016.04.041

Cited by 88 publications

(51 citation statements)

References 26 publications

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“…To do so, the individual sets of data undergo training, validation, verification, and testing. The principle behind the ML modeling workflow and the strategy for flood modeling are described in detail in the literature [48,65]. Figure 2 represents the basic flow for building an ML model.…”

Section: State Of the Art Of ML Methods In Flood Predictionmentioning

confidence: 99%

“…described how ML techniques could efficiently model complex hydrological systems such as floods. Many ML algorithms, e.g., artificial neural networks (ANNs) [44], neuro-fuzzy [45,46], support vector machine (SVM) [47], and support vector regression (SVR) [48,49], were reported as effective for both short-term and long-term flood forecast. In addition, it was shown that the performance of ML could be improved through hybridization with other ML methods, soft computing techniques, numerical simulations, and/or physical models.…”

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confidence: 99%

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Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

1,030

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: State Of the Art Of ML Methods In Flood Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

1,030

469

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“…polynomial, sigmoid or RBF, can be used in nonlinear case. RBF and sigmoid kernels used in this paper are defined as presented in [11,[15][16][17][18]:…”

Section: Support Vector Machines For Regressionmentioning

confidence: 99%

“…Hosseini and Mahjouri (2016) presented a new rainfall-runoff model called SVR-GANN, where the SVR model is combined with a geomorphology-based ANN model in a case study of three sub-basins located in a semiarid region in Iran [15]. Gizaw and Gun (2016) developed the Regional Flood Frequency Analysis (RFFA) model based on SVR to estimate regional flood quantiles for two study areas in Canada [16]. He et al (2014) compared ANFIS and SVM for forecasting river flow in a semiarid mountain region in north-western China [17].…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for hydrological modelling in Karst

Lakušić¹

2018

JCE

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Application of artificial neural networks for hydrological modelling in karstThe possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural network, radial basis function neural network, support vector machines for regression (SVR), and adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS model outperforms other algorithms when the root mean square error and mean absolute error are used as quality indicators. Primjena neuronskih mreža za hidrološko modeliranje u krškom području U ovom radu je razmatrana mogućnost kratkoročnog predviđanja protoka vode u krškom području. Četiri suvremena algoritma strojnog učenja su korištena za predviđanje protoka jedan dan unaprijed i to: neuronska mreža s višeslojnim perceptronom, neuronska mreža s radijalnom aktivacijskom funkcijom, metoda potpornih vektora za regresiju (SVR), adaptivni neuroneizraziti (fuzzy) sustav zaključivanja (ANFIS). Rezultati pokazuju da ANFIS model nadmašuje ostale algoritme kada se korijen srednje kvadratne popogreške i srednja apsolutna pogreška upotrijebe kao mjere kvalitete.

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“…The main advantage of SVM is that it not only possesses the strength of ANN but can overcome some of its major problems such as local minimum and network over fitting (ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000). Although SVM has been used in many areas successfully (Yu et al, 2006;Lin et al, 2006;Wang et al, 2009;Wang et al, 2013;Niakm and Gupta, 2013;Gizaw and Gan, 2016), its output depends on the selection of a suitable kernel function and parameters. The hyper parameters of SVM are heuristic and generally selected by a time-consuming trial and error process (Deka, 2014).…”

Section: Introductionmentioning

confidence: 99%