A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India

Sahana, Mehebub; Patel, Priyank Pravin

doi:10.1007/s12665-019-8285-1

Cited by 124 publications

(52 citation statements)

References 103 publications

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“…Basically, the approach is based on expert opinion on the environmental setting of the study area. An advantage of the fuzzy logic model is that the model allows more flexible combinations of the weighted maps and can be used at any scale of analysis [41,71]. The fuzzy sets can be edited very fast, so it is very easy to try various scenarios, to add or remove new data layers to the model with GIS software and to minimize the effects of over-estimated membership values.…”

Section: Discussionmentioning

confidence: 99%

“…The FR model is a quantitative technique that defines the correlation between the phenomenon and the factors related to it in a spatial perspective; it consists in the computation of the ratio of occurrence to nonoccurrence probability for a specific phenomenon [41]. In this work, a Frequency Ratio (FR) model was used to examine subsidence susceptibility as it derives a quantified correlation between the subsidence area inventory and the conditioning factors.…”

Section: Frequency Ratio (Fr)mentioning

confidence: 99%

“…The FR method was chosen as a fast and precise statistical approach in terms of success rate with good predictive ability-especially in low occurrence areas [5,40]. It is a robust model that can be applied very simply [41]. The FL method was chosen as an expert knowledge-based approach that avoids linear monotonic relationship between susceptibility and causal factors assumed by bivariate statistical data-driven models [42].…”

Section: Introductionmentioning

confidence: 99%

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Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic

et al. 2019

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This study aimed at evaluating and mapping Ground Subsidence Susceptibility (GSS) in the Grosseto plain (Tuscany Region, Italy) by exploiting multi-temporal satellite InSAR data and by applying two parallel approaches; a bivariate statistical analysis (Frequency Ratio) and a mathematical probabilistic model (Fuzzy Logic operator). The Grosseto plain experienced subsidence and sinkholes due to natural causes in the past and it is still suffering slow-moving ground lowering. Five conditioning subsidence-related factors were selected and managed in a GIS environment through an overlay pixel-by-pixel analysis. Firstly, multi-temporal ground subsidence inventory maps were prepared in the study area by starting from two inventories referred to distinct temporal intervals (2003–2009 and 2014–2019) derived from Persistent Scatterers Interferometry (PSI) data of ENVISAT and SENTINEL-1 satellites. Then, the susceptibility modelling was performed through the Frequency Ratio (FR) and Fuzzy Logic (FL) approaches. These analyses led to slightly different scenarios which were compared and discussed. Results show that flat areas on alluvial and colluvial deposits with thick sedimentary cover (higher than 20 m) on the bedrock in the central and eastern sectors of the plain are the most susceptible to land subsidence. The obtained FR- and FL-based GSS maps were finally validated with a ROC (Receiver Operating Characteristic) analysis, in order to estimate the overall performance of the models. The AUC (Area Under Curve) values of ROC analysis of the FR model were higher than the ones of FL model, suggesting that the former is a better and more appropriate predictor for subsidence susceptibility analysis in the study area. In conclusion, GSS maps provided a qualitative overview of the subsidence scenarios and may be helpful to predict and preliminarily identify high-risk areas for environmental local authorities and decision makers in charge of land use planning in the study area. Finally, the presented methodologies to derive GSS maps are easily reproducible and could also be applied and tested in other test sites worldwide, in order to check the modeling performance in different environmental settings.

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Section: Discussionmentioning

confidence: 99%

Section: Frequency Ratio (Fr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic

et al. 2019

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“…To evaluate the flood vulnerability, it was necessary to investigate a series of flood triggering and causal factors and their relationship with flooding [51,52]. In past studies, different floodcontrolling factors have been used [1,12,13].…”

Section: Identification Of Flood Triggering and Causal Factorsmentioning

confidence: 99%

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

2020

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Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate floodprone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

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“…Statistical approaches are quantitative methods which can use different functional relationship. They can be subdivided into: (1) physically-based methods [10,11]; and (2) traditional statistical methods, such as the frequency ratio [12,13], evidential belief function [14,15], weight of evidence [16,17], discriminant analysis [18][19][20][21], and logistic regression [22,23], (3) advanced data mining technologies, such as artificial neural networks [24,25], support vector machines [26][27][28], adaptive neuro-fuzzy inference systems [29,30], alternating decision trees [31], and functional trees [32,33]. These methods need landslide inventories expressed as landslide density maps to produce functional relationships with causative factors [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Wang

Chen

2020

IJGI

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The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area.

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A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India

Cited by 124 publications

References 103 publications

Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic

Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

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