Flooding and its relationship with land cover change, population growth, and road density

Rahman, Mohd Tadza Abdul; Chen, Ningsheng; Mahmud, Golam Iftekhar; Islam, Monirul; Pourghasemi, Hamid Reza; Ahmad, Hilal; Habumugisha, Jules Maurice; Washakh, Rana Muhammad Ali; Alam, Mehtab; Liu, Enlong; Han, Zheng; Ni, Huayong; Tian, Shufeng; Dewan, Ashraf

doi:10.1016/j.gsf.2021.101224

Cited by 126 publications

(54 citation statements)

References 93 publications

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“…NDVI, rainfall, and altitude were ranked in the next orders. The result concurs withHong et al (2018) andRahman et al (2021) findings who acknowledged the significant role of lithology and land use on flood occurrence. The importance of lithology, land use, and altitude was also highlighted byRahmati et al (2016).…”

supporting

confidence: 91%

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Rezaie

Panahi

Bateni

et al. 2022

Preprint

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Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood risk mapping (FRM) is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility (FSMs). The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. The statistical measures of RMSE, MAE, AUC, and ROC curve were then applied for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for school flood risk mapping (SFRM).

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supporting

confidence: 91%

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Rezaie

Panahi

Bateni

et al. 2022

Preprint

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“…The result shows that the AUC value for the proposed model was 0.904 (Figure 12), which is acceptable. Thus the model can reasonably be concluded to have excellent prediction capability for the construction of an urban flood resilience map [67,75].…”

Section: Model Validation and Performance Evaluationmentioning

confidence: 77%

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

et al. 2021

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Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.

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“…Moreover, Sixth Assessment Report (AR6) by the Intergovernmental Panel on Climate Change (IPCC) stated that the heavy precipitation events would become more frequent and intense than in the recent past on a global and continental scale and translates to an increase in frequency and magnitude of pluvial floods such as surface water and flash floods with a high degree of confidence (Seneviratne et al, 2021). Further, various studies have also highlighted the influence of anthropogenic and geomorphological factors such as the role of land use and land cover changes on flooding, flood intensification, and losses, in addition to hydrometeorological factors (Adnan et al, 2020;Rahman et al, 2021;Rogger et al, 2017;Roy et al, 2020;Saghafian et al, 2008). Flood changes at a catchment scale could be caused by changes in forest management, agricultural practices, and infrastructural developments (Hajian et al, 2019;Pal et al, 2021;Rogger et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood

Sherpa

Shirzaei

2021

J Flood Risk Management

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Extreme precipitation and flooding often lead to human and economic losses. However, the high-resolution nationwide flooding exposure data are scarce.Availability of all-weather space-borne SAR satellite data can potentially improve the ability to generate high-resolution flood map extent and exposure globally. In Iran, flooding is a major concern, given the socioeconomic vulnerabilities and increased likelihood of climate extremes. Iran experienced extreme flooding during January to March 2019, attributed to significant precipitation during October 2018 to March 2019, which is well above the longterm averages for 1999-2019. Using Pettitt and Mann Kendall tests, nationwide precipitation records were identified by significant decreasing and increasing trends in north and south, respectively. Utilizing 673 Sentinel-1 SAR intensity images, we applied a fast-marching algorithm for image segmentation in combination with a Bayesian framework to obtain high-resolution probabilistic flood maps. We found, 22, 9, and 15 states in January, February, and March, respectively, experienced flooding that covered >15% of their area with high flooded area percent in the northwestern and southeastern region. We estimated that >15, >11.32, and >11.33 million people were exposed to floods in January, February, and March, respectively. Our datasets inform flooding models and management efforts under increasing climate extremes and changing land use and cover.

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Flooding and its relationship with land cover change, population growth, and road density

Cited by 126 publications

References 93 publications

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood

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