Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting

Yu, Pao Shan; Yang, Tao; Chen, Szu Yin; Kuo, Chen Min; Tseng, Hung Wei

doi:10.1016/j.jhydrol.2017.06.020

Cited by 201 publications

(98 citation statements)

References 33 publications

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“…Figure 2 represents the basic flow for building an ML model. The major ML algorithms applied to flood prediction include ANNs [66], neuro-fuzzy [67], adaptive neuro-fuzzy inference systems (ANFIS) [68], support vector machines (SVM) [69], wavelet neural networks (WNN) [70], and multilayer perceptron (MLP) [71]. In the following subsections, a brief description and background of these fundamental ML algorithms are presented.…”

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

confidence: 99%

“…prediction is yet to be fully investigated [134]. The random forests (RF) method [69,135] is another popular DT method for flood prediction [136]. RF includes a number of tree predictors.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

“…Each individual tree creates a set of response predictor values associated with a set of independent values. Furthermore, an ensemble of these trees selects the best choice of classes [69]. Reference [137] introduced RF as an effective alternative to SVM, which often delivers higher performance in flood prediction modeling.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

See 2 more Smart Citations

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

1,034

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%

“…prediction is yet to be fully investigated [134]. The random forests (RF) method [69,135] is another popular DT method for flood prediction [136]. RF includes a number of tree predictors.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

See 1 more Smart Citation

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

1,034

469

View full text Add to dashboard Cite

show abstract

“…For example, ANNs have been successfully employed to forecast rainfall and flood for decades [1][2][3][4][5][6][7]. Support vector machines (SVMs), which have the same network architecture as the radial basis function neural network [8], have recently gained popularity and exhibited good performance in rainfall and flood forecasting [9][10][11][12][13][14][15][16][17]. In addition, the neurofuzzy system, which combines the ANN and fuzzy inference system, has been favorably applied in various hydrologic forecasting studies [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Jhong

Chen

Lin

et al. 2018

Water

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This study proposed a hybrid neural network model that combines a self-organizing map (SOM) and back-propagation neural networks (BPNNs) to model the rainfall-runoff process in a physically interpretable manner and to accurately forecast typhoon floods. The SOM and a two-stage clustering scheme were applied to group hydrologic data into four clusters, each of which represented a meaningful hydrologic component of the rainfall-runoff process. BPNNs were constructed for each cluster to achieve high forecasting capability. The physical hybrid neural network model was used to forecast typhoon flood discharges in Wu River in Taiwan by using two types of rainfall data. The clustering results demonstrated that the rainfall-runoff process was favorably described by the sequence of derived clusters. The flood forecasting results indicated that the proposed hybrid neural network model has good forecasting capability, and the performance of the models using the two types of rainfall data is similar. In addition, the derived lagged inputs are hydrologically meaningful, and the number and activation function of the hidden nodes can be rationally interpreted. This study also developed a traditional, single BPNN model trained using the whole calibration data for comparison with the hybrid neural network model. The proposed physical hybrid neural network model outperformed the traditional neural network model in forecasting the peak discharges and low flows.

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“…The models of machine learning algorithms have been applied to solve the real-time rainfall prediction problem. Support vector machine (SVM) shows better performance than random forest (RF) in 2 and 3 h ahead prediction [10]. Artificial neural networks (ANNs), such as radial basic function neural networks (RBFNNs) and back propagation neural networks (BPNNs), also perform satisfactorily in some cases.…”

Section: Introductionmentioning

confidence: 99%

Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors

Chao

Yin

et al. 2018

Journal of Sensors

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A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems' (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.

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Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting

Cited by 201 publications

References 33 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors

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