Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

Rahman, Mahfuzur; Chen, Ningsheng; Islam, Monirul; Dewan, Ashraf; Iqbal, Javed; Washakh, Rana Muhammad Ali; Tian, Shufeng

doi:10.1007/s41748-019-00123-y

Cited by 204 publications

(85 citation statements)

References 67 publications

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“…Compared to other studies, the proposed method not only enables the timely analysis of long and dense time-series with a large number of scenes but also significantly contributes to satellite-based emergency mapping activities, where rapid analysis over synoptic areas and across diverse data sources is a crucial criterion. Flood duration products have not been considered in the context of emergency mapping to date, due to a lack of automation of existing methods [20][21][22] and often limited data availability from the onset of a disaster. Existing studies have not specifically considered this aspect and focused only on using data from single sensors [22][23][24][25][26]29,30] or sensors groups of optical [27] or SAR [20,21] missions.…”

Section: Discussionmentioning

confidence: 99%

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

2020

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Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India.

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Section: Discussionmentioning

confidence: 99%

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

2020

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“…Given the focus of this study was the presentation of the HST approach to calibrate SAR inundation extents rather than to describe the modeling procedures, brief summaries of the model setup and validation are provided below. References [11,30] provide more detailed descriptions of the model setup.…”

Section: Inundation Hydraulic Modelingmentioning

confidence: 99%

“…In flood studies, which integrate SAR images and hydrological/hydraulic models, SAR data are commonly used to calibrate and validate hydrological and hydraulic models [4,[9][10][11]. However, fewer studies have inverted this process and used the results of hydrological/hydraulic models to calibrate remote sensing-based flood extractions.…”

Section: Introductionmentioning

confidence: 99%

Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

Quang

Tuấn

Hằng

et al. 2019

Water

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Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS’s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of −15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014–2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC).

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“…A flood susceptibility map will show areas where floods are likely to occur. Flood susceptibility is defined as a quantitative or qualitative assessment of an area with spatial distribution of flood, where probability of flood occurrence is likely [9]. This is a measure of the probability of future floods likely to occur depending on meteorological conditions [10].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

109

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

Cited by 204 publications

References 67 publications

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

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