Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models

Arora, Aman; Pandey, Manish; Siddiqui, Masood Ahsan; Hong, Haoyuan; Mishra, Varun Narayan

doi:10.1080/10106049.2019.1687594

Cited by 121 publications

(49 citation statements)

References 150 publications

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“…Recently, various statistical machine learning techniques have been developed, including Frequency Ratio Index (FR) for flood risk mapping in the Markam Basin of Papua New Guinea [31], and flood sensitivity modeling in part of the Middle Ganga Plain in the Ganga Land Basin [32]. A number of studies have investigated the ability, and the effectiveness, of machine learning approaches combined with various optimization techniques for forecasting flash flood risk such as a combined artificial neural network (FA-LM-ANN) model in the Bac Ha Region located in Northwest Vietnam [33] and flood prediction using a self-organized neural network (SOM) technique at Kemaman River in Malaya Peninsula [34].…”

Section: Introductionmentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

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Section: Introductionmentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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“…The major concentration of high to very high susceptible zones can be observed in the complete eastern MGP where the major concentration of total annual average rainfall (>1,100 mm) is being recorded. The monsoon rainfall hits the area in the last of June and early July, submerged the low altitude basins first and causes an unprecedented situation (Arora et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The colossal loss of properties and agricultural products happen in the recurring period is recorded in various government reports for this study area. The study area is the confluence zone of major rivers‐ Ghaghara, Gandak, Ganga, Son, Kosi, and other minor tributaries of Ganga (Arora, Pandey, Siddiqui, Hong, & Mishra, 2019). Hence, the risk of inundation during monsoon period is much higher than other regions of India.…”

Section: Study Areas and Data Setmentioning

confidence: 99%

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

Ahmadlou

Al-Fugara

Al‐Shabeeb

et al. 2020

J Flood Risk Management

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Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.

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“…All the data were processed in a GIS environment which has been regarded as an excellent tool in spatial management and data manipulation [12]. Studies on past research [118][119][120][121][122][123] showed that they succeeded in modeling various natural disasters (e.g., landslides, floods, volcanic eruptions, forest fire, tsunamis) using FR or IoE or the combination of both and AHP either for mitigation, susceptibility, vulnerability, hazard or risk purposes with good model performances.…”

Section: Seismic Vulnerability Studies In the Study Areamentioning

confidence: 99%

Seismic Vulnerability Assessment in Ranau, Sabah, Using Two Different Models

Razak¹,

Rambat²,

Ros³

et al. 2021

IJGI

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Sabah is prone to seismic activities due to its location, being geographically located near the boundaries of three major active tectonic plates; the Eurasian, India-Australia, and Philippine-Pacific plates. The 6.0 Mw earthquake that occurred in Ranau, Sabah, on 15 June 2015 which caused 18 casualties, all of them climbers of Mount Kinabalu, raised many issues, primarily the requirements for seismic vulnerability assessment for this region. This study employed frequency ratio (FR)–index of entropy (IoE) and a combination of (FR-IoE) with an analytical hierarchy process (AHP) to map seismic vulnerability for Ranau, Sabah. The results showed that the success rate and prediction rate for the areas under the relative operating characteristic (ROC) curves were 0.853; 0.856 for the FR-IoE model and 0.863; 0.906 for (FR-IoE) AHP, respectively, with the highest performance achieved using the (FR-IoE) AHP model. The vulnerability maps produced were classified into five classes; very low, low, moderate, high, and very high seismic vulnerability. Seismic activities density ratio analysis performed on the final seismic vulnerability maps showed that high seismic activity density ratios were observed for high vulnerability zones with the values of 9.119 and 8.687 for FR-IoE and (FR-IoE) AHP models, respectively.

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Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models

Cited by 121 publications

References 150 publications

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

Seismic Vulnerability Assessment in Ranau, Sabah, Using Two Different Models

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