Satellite data planning for flood mapping activities based on high rainfall events generated using TRMM, GEFS and disaster news

Sharma, Vinod K.; Mishra, Nitin; Shukla, Amit; Yadav, Anil Kumar; Rao, G. Srinivasa; Bhanumurthy, V.

doi:10.1080/19475683.2017.1304449

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…Another factor which considerable mentioned in the literature and plays a pivotal role in flood susceptibility mapping is rainfall (Breinl et al, 2021;Das and Scaringi, 2021;Sharma et al, 2017). There is a significant association between the rainfall and flooding throughout the area under study.…”

Section: Rainfallmentioning

confidence: 99%

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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Section: Rainfallmentioning

confidence: 99%

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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“…Afew studies have explored using rainfall data from satellite data such as Tropical Rainfall Measurement Mission (TRMM) to overcome this gap (Indu and Nagesh Kumar, 2014;Mondal et al, 2018). Attempts have also been made to integrate the satellite data with forecasting systems to improve the performance of the models in predicting floods (Sharma et al, 2017;Mitra et al, 2013). However, there are high uncertainties associated with precipitation, and still gaps exist in precisely quantifying the uncertainties in literature.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

Carbon Soot for Electrochemical Energy Storage Applications

Rajulapati¹,

Mujumdar

2019

PINSA

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Flood modelling is an important first step towards understanding and managing floods, at all spatial scales, from a large catchment of a river to highly developed urban areas and to individual property and infrastructure. Recent advances in modelling techniques along with sophisticated computational tools and data products have facilitated a rapid progress in flood modelling. Despite this progress, an accurate assessment and forecasting of floods with the associated risk is still elusive due to uncertainties at each stage of the modelling. This paper presents a review of flood modelling with specific focus on India. In recent years, significant contributions have been made by Indian researchers to the hydrologic science in general, and to flood modelling in particular. Indian contributions to the areas of hydrologic modelling of floods, flood forecasting, flash flood modelling, urban floods, and risk assessment and mitigation are reviewed in the paper. Since the emphasis is on flood modelling, the related topics of flood frequency analysis are excluded, although some representative studies on extreme rainfall frequencies are briefly mentioned. The paper concludes with some perspectives on future directions in flood modelling and actions needed for sustainable flood management practices, in the country.

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“…Arabindoo (2016) discussed the argument among environmentalists and the administration where the former targeted the haphazard urbanization as a strong reason for the floods in the city and the later considered it as an unprecedented climate anomaly in the history of the city. Sharma et al (2017) showed the potential of global rainfall products (Tropical Rainfall Measuring Mission (TRMM)) and global forecast precipitation products (Global Ensemble Forecast System (GEFS) in successful flood mapping of the region. Methods such as map and site analysis, focus group interview and questionnaire survey were employed by Muneerudeen (2017) to compose urban and landscape design strategies for flood resilience.…”

Section: Chennai Citymentioning

confidence: 99%

Flood risk and adaptation in Indian coastal cities: recent scenarios

et al. 2018

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Coastal cities contrive to spread their transformative influence both into the hinterland, along the coastline, and into the coastal waters themselves. These effects will be intensified in urban agglomerations as the concentration of population and allied activities are more pronounced there compared to the inland regions. Indian coastal cities are no exception, and it is high time to delineate these hazard-prone regions and implement proper mitigation and adaptation strategies at city scale. This review article provides an assessment regarding quantification, management and climate change impacts of flood risks in Surat, Mumbai, Chennai and Kolkata, which are the most populated coastal cities in India. The flood impacts considered in the existing or prevailing analyses are associated with adverse effects on population, land use of cities, transportation and economy caused by different types of riverine and urban flooding, though coastal flooding, tsunami and storm surge effects are less studied. Mumbai and Kolkata are relatively progressive in the assessment of flood risks and adaptation. The present article also suggests strategies to evaluate the relative progress in the assessment of past and future risks and adaptation. We also discuss the mitigation and adaptation strategies considering the historical importance of these cities. We propose that the strategies should be implemented considering public opinion and should be initialized at the grass root level. Though it is technically difficult to re-plan the city structures in the current scenario, it is possible to adapt to and mitigate the effects of natural hazards through suitable planning and management with the integrated cooperation and involvement of citizens and government as well.

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Satellite data planning for flood mapping activities based on high rainfall events generated using TRMM, GEFS and disaster news

Cited by 17 publications

References 19 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

Carbon Soot for Electrochemical Energy Storage Applications

Flood risk and adaptation in Indian coastal cities: recent scenarios

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