Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

doi:10.1016/j.jhydrol.2013.09.034

Cited by 696 publications

(329 citation statements)

References 66 publications

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“…While many classification techniques exist, the quantile method was chosen for this purpose, based on its popularity (Tehrany et al 2013). Altitude, slope, SPI, TWI, NDVI, rainfall, distance from river and distance from road were categorized into 10 equal area classes.…”

Section: Methodsmentioning

confidence: 99%

“…Compared to some methods such as machine learning techniques (Chen et al, 2017a;Chen et al, 2017b;Chen et al, 2017d), statistical methods can be easily understood and their processing time is considerably quicker, which makes them appropriate for catastrophe mapping (Tehrany et al 2013). However, among statistical methods, some are more thorough in catchment and flood mapping than others.…”

Section: Introductionmentioning

confidence: 99%

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GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

221

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

221

View full text Add to dashboard Cite

show abstract

“…Although many of the studies related to catastrophic events have focused on analysis and prevention, the development of machine learning techniques allowed some investigations to estimate the probability of events using statistical or machine learning methods. For example, Tehrany et al have used decision tree method to predict flood susceptible areas and have resulted in up to 90% accuracy in validation [10]. M.-L. Guillerminet have used decision tree to predict the case of the possible collapse of the West Antarctic Ice Sheet [11].…”

Section: Fig 1 Number Of Interphase Spacers Installed and Numbermentioning

confidence: 99%

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

Lee¹,

Jung²,

Koo³

et al. 2017

Journal of Electrical Engineering and Technology

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-Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

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“…Flood management can be categorized into several stages: susceptibility, hazard, vulnerability and risk analysis (Sharma et al, 2010). The primary need in performing any sort of flood modelling is having a flood inventory map and a set of flood causative factors (Tehrany et al, 2013). The necessity of producing flood inventory maps is to record and document the size, coverage and trend of the inundated areas.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Variations in the Flood Susceptibility Maps Accuracies Due to the Alterations in the Type and Extent of the Flood Inventory

Tehrany

Jones

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63%, 76%, 88%, 80%, 74%, 71% and 65% respectively. Evidently, using the polygon format of the inventory data didn't lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.

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Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

Cited by 696 publications

References 66 publications

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

Evaluating the Variations in the Flood Susceptibility Maps Accuracies Due to the Alterations in the Type and Extent of the Flood Inventory

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