An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Choubin, Bahram; Moradi, Ehsan; Golshan, Mohammad; Adamowski, Jan; Hosseini, Farzaneh Sajedi; Mosavi, Amir

doi:10.1016/j.scitotenv.2018.10.064

Cited by 609 publications

(241 citation statements)

References 60 publications

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“…In the past decades, many notable ML methods, such as ANN [74], ANFIS [68,192], SVM [193], SVR [193], WNN [51], and bootstrap-ANN [51], were used for long lead-time predictions with promising results. Recently, in a number of studies (e.g., References [55,[194][195][196][197][198]), the performances of various ML methods for long lead-time flood predictions were compared. However, it is still not clear which ML method performs best in long-term flood prediction.…”

Section: Long-term Flood Prediction With MLmentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

Self Cite

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: Long-term Flood Prediction With MLmentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

Self Cite

1,034

469

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“…Identifying the conditioning factors is a key step for flood susceptibility assessment. Thirteen conditioning factors were selected in this study by reviewing previous studies and investigating the mechanisms of flood, including maximum daily precipitation (MDP), precipitation concentration degree (PCD), altitude, slope, relief degree of land surface (RDLS), soil type (ST), Manning coefficient (MC), proportion of forest and shrubland (PFS), proportion of artificial surface (PAS), proportion of cropland (PC), drainage density (DD), population, and gross domestic product (GDP) [19,[27][28][29]. These data are calculated based on data such as digital elevation and land use.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…Slope is an important geomorphological feature that triggers flood disasters [19,41]. Slope directly affects the generation of surface runoff and the infiltration of precipitation, and river basins with large slopes in mountainous areas are often more prone to flood disaster.…”

Section: Slopementioning

confidence: 99%

“…For example, some studies have used multi-criteria decision analysis methods, including the analytic hierarchy process and the expert scoring method [16][17][18]. These methods are based on expert knowledge and are susceptible to uncertainty [19]. Physically based models such as VIC and MIKE models at a regional scale, and other hydrological models at the continental and global scale have also been used to study floods, and have shown great advantages in regional or global flood process research [8,[20][21][22][23].…”

mentioning

confidence: 99%

“…This is its advantage over machine learning models. However, in the face of the complexity of global floods, flood models at a global scale often require a large number of model parameters, repeated model debugging, and long computing time [19,[21][22][23][26][27][28]. Machine learning methods may be a good choice for faster access to global flood hazard assessments.…”

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Flood Risk Assessment of Global Watersheds Based on Multiple Machine Learning Models

Yan

Wang

et al. 2019

Water

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Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

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Ensemble learning of daily river discharge modeling for two watersheds with different climates

Zhang²,

Liu

et al. 2020

Atmospheric Science Letters

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In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography-based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semiarid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash-Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising.

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An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Cited by 609 publications

References 60 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Flood Risk Assessment of Global Watersheds Based on Multiple Machine Learning Models

Ensemble learning of daily river discharge modeling for two watersheds with different climates

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