Assessing the susceptibility of schools to flood events in Iran

Yousefi, Saleh; Pourghasemi, Hamid Reza; Emami, Sayed Naeim; Rahmati, Omid; Tavangar, Shahla; Pouyan, Soheila; Tiefenbacher, John P.; Shamsoddini, Shahbaz; Nekoeimehr, Mohammad

doi:10.1038/s41598-020-75291-3

Cited by 21 publications

(8 citation statements)

References 93 publications

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Section: Discussionmentioning

confidence: 99%

“…In addition, the kernel parameters play an important role in determining the distance between patterns, dimensions and the complexity of new spaces of the classification model [57]. Therefore, the work described here is intended to build on recent research showing that the SVM algorithm is more accurate than alternative methods [11,28,33,[58][59][60][61]. One of the most important advantages of the SVM classification method for land use mapping is the ability to export classified images with high accuracy from small training sets [1,14,60,62].…”

Section: Discussionmentioning

confidence: 99%

“…Most major natural hazard modeling requires a land use map [60][61][62]. These include hydrologic modeling [26,63], soil erosion modelling [64,65], land slide modelling [66,67], snow avalanche modelling [68,69], fluvial geomorphology [7], land scape modelling [65,70], gully erosion [71] and flood modelling [72][73][74][75].…”

Section: Discussionmentioning

confidence: 99%

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Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Yousefi

Mirzaee

Almohamad

et al. 2022

Land

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Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Yousefi

Mirzaee

Almohamad

et al. 2022

Land

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“…Flood events including the rise of sea level, specifically, large-scale floods are caused by climate change (Cadag et al, 2017). The most considered destructive climate-related hazards in the world are floods and children dwelling and studying in schools located in the flooded areas (Ardales Jr, Espaldon, Lasco, Quimbo, & Zamora, 2016;Yousefi et al, 2020). These hazards can be caused by natural or human-induced activities including heavy rains, melting snow, modified drainage networks, failing dams and manipulation of water-absorbing forests (Yousefi et al, 2020).…”

Section: Coastal Schools' Safety Proceduresmentioning

confidence: 99%

Implementation of school disaster risk reduction in coastal schools: Basis for a plan of action

Cresencio

Yabut²

2023

JEELR

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School Disaster Risk Reduction Management (SDRRM) plays a crucial role in ensuring that learners, teachers and other school personnel are safe on the school premises. This study evaluated how well coastal schools in Cluster VII of the Division of Pampanga, Philippines had implemented a school disaster risk reduction management system. This study used the descriptive research method. Respondents to the study were seventy-four (74) public school teachers during the school year 2020-2021. In this research, the following findings were obtained: The respondents assessed safe learning facilities, school disaster management, risk reduction and resilience education and coastal school safety procedures. However, among these categories “coastal school safety procedures” got the lowest overall mean score. Additional findings showed that there is no discernible variation in the degree of SDRRM implementation across the respondent schools. This means that these schools have uniformity in implementing school disaster risk reduction management. It is recommended that schools maintain uniformity in the implementation of DRR policies.

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“…Such circumstances hinder groundwater recharge 3 , perturb evapotranspiration losses 4 , and accumulate surface runoff, raising the risk of floods 3 , 5 . Climate change, particularly precipitation extremes, is another factor that exposes populations and infrastructure to urban flooding, with a significant impact on poor people, children, and critical infrastructure 6 , 7 . Therefore, it is necessary to continually look at the nexus between urban growth, climate change, and flood risk.…”

Section: Introductionmentioning

confidence: 99%

Exploring urban growth–climate change–flood risk nexus in fast growing cities

Ajjur

Al-Ghamdi

2022

Sci Rep

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This study looks at the nexus between urban growth, climate change, and flood risk in Doha, Qatar, a hot-spot, climate change region that has experienced unprecedented urban growth during the last four decades. To this end, this study overviews the main stages of Doha’s urban growth and influencing climatic factors during this period. A physically-based hydrological model was then built to simulate surface runoff and quantify flood risk. Finally, the Pearson correlation was used to verify the potential nexus between flood risk, climate change, and urban growth. Surveying showed that, between 1984 and 2020, urban areas grew by 777%, and bare lands decreased by 54.7%. In addition, Doha witnessed various climatic changes with a notable increase in air temperature (+ 8.7%), a decrease in surface wind speed (− 19.5%), and a decrease in potential evapotranspiration losses (− 33.5%). Growth in urban areas and the perturbation of climatic parameters caused runoff to increase by 422%, suggesting that urban growth contributed more than climatic parameters. Pearson correlation coefficient between flood risk and urban growth was strong (0.83) and significant at p < 0.05. Flood risk has a strong significant positive (negative) correlation with air temperature (wind speed) and a moderate positive (negative) correlation with precipitation (potential evapotranspiration). These results pave the way to integrate flood risk reduction measures in local urban development and climate change adaptation plans.

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Assessing the susceptibility of schools to flood events in Iran

Cited by 21 publications

References 93 publications

Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

Implementation of school disaster risk reduction in coastal schools: Basis for a plan of action

Exploring urban growth–climate change–flood risk nexus in fast growing cities

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