Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Abstract Identifying areas prone to flooding is a key step in flood hazard management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the altitude and distance from the river are the most important variables for assessing flooding susceptibility.

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A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling

10.21203/rs.3.rs-202961/v1 ◽

2021 ◽

Author(s):

Ehsan Shahiri Tabarestani ◽

Hossein Afzalimehr

Keyword(s):

Characteristic Curve ◽

Drainage Density ◽

Weight Of Evidence ◽

Mazandaran Province ◽

Conditioning Factors ◽

Flood Susceptibility ◽

Susceptibility Modeling ◽

Susceptibility Maps ◽

Reliable Model ◽

First Time

Abstract Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

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Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Frontiers in Earth Science ◽

10.3389/feart.2021.659296 ◽

2021 ◽

Vol 9 ◽

Author(s):

Manish Pandey ◽

Aman Arora ◽

Alireza Arabameri ◽

Romulus Costache ◽

Naveen Kumar ◽

...

Keyword(s):

Machine Learning ◽

Regression Tree ◽

Classification And Regression Tree ◽

Ground Subsidence ◽

Ensemble Model ◽

Ganga Plain ◽

Humid Climate ◽

Area Index ◽

Flood Susceptibility ◽

Middle Ganga Plain

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

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Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

Open Geosciences ◽

10.1515/geo-2020-0329 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1668-1688

Author(s):

Azemeraw Wubalem ◽

Gashaw Tesfaw ◽

Zerihun Dawit ◽

Belete Getahun ◽

Tamrat Mekuria ◽

...

Keyword(s):

Land Use Planning ◽

Analytical Hierarchy Process ◽

Flood Hazard ◽

Google Earth ◽

Information Value ◽

Lake Tana ◽

Ahp Model ◽

Flood Susceptibility ◽

Susceptibility Modeling ◽

Hierarchy Process

Abstract The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.

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Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection

Handbook of Statistics - Cognitive Computing: Theory and Applications ◽

10.1016/bs.host.2016.07.007 ◽

2016 ◽

pp. 229-251

Author(s):

S. Chakraborty

Keyword(s):

Regression Tree ◽

Seemingly Unrelated Regression ◽

Unrelated Regression ◽

Additive Regression

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Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan

Environmental Earth Sciences ◽

10.1007/s12665-018-8041-y ◽

2019 ◽

Vol 78 (1) ◽

Cited By ~ 18

Author(s):

Shakeel Mahmood ◽

Atta-ur Rahman

Keyword(s):

Flash Flood ◽

Flood Susceptibility ◽

Susceptibility Modeling ◽

Hindu Kush

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Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

Remote Sensing ◽

10.3390/rs12213568 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3568

Author(s):

Shahab S. Band ◽

Saeid Janizadeh ◽

Subodh Chandra Pal ◽

Asish Saha ◽

Rabin Chakrabortty ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Flash Flood ◽

Flood Hazard ◽

Machine Learning Algorithms ◽

Mitigation Strategies ◽

Boosted Regression Tree ◽

Flash Flooding ◽

Flood Susceptibility ◽

Susceptibility Modeling

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

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Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

Remote Sensing ◽

10.3390/rs12213620 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3620

Author(s):

Indrajit Chowdhuri ◽

Subodh Chandra Pal ◽

Alireza Arabameri ◽

Asish Saha ◽

Rabin Chakrabortty ◽

...

Keyword(s):

Land Use Changes ◽

Area Under The Curve ◽

Regression Tree ◽

Gully Erosion ◽

Support Vector ◽

Boosted Regression Tree ◽

Conditioning Factors ◽

Susceptibility Maps ◽

Chotanagpur Plateau ◽

Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

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Estimating subnational preferences across the European Union

Political Science Research and Methods ◽

10.1017/psrm.2019.38 ◽

2019 ◽

pp. 1-9

Author(s):

Jana Lipps ◽

Dominik Schraff

Keyword(s):

National Level ◽

Regression Tree ◽

Data Availability ◽

The European Union ◽

Political Preferences ◽

Nationally Representative ◽

The Rich ◽

Additive Regression ◽

Promising Source ◽

Adequate Data

AbstractSubnational analyses of political preferences are substantively relevant and offer advantages for causal inference. Yet, our knowledge on regional political preferences across Europe is limited, not least because there is a lack of adequate data. The rich Eurobarometer (EB) data is a promising source for European-wide regional information. Yet, it is only representative for the national level. This paper compares state-of-the-art methods for estimating regional preferences from nationally representative EB data, validating predictions with regionally representative surveys. Our analysis highlights a number of challenges for estimating regional preferences across Europe, such as data availability, variable selection, and over-fitting. We find that predictions are best using a Bayesian additive regression tree with synthetic post-stratification.

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