Building Resilience and Management of Vulnerability: Solution for Reduction of Risk of Disasters

Khan, Nazoora; Shamim, Syed Kausar

doi:10.1007/978-981-19-3567-1_21

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The Hindu Kush mountain range acts as a barrier, influencing weather patterns and causing variations in temperature and precipitation across different regions of Pakistan. Additionally, the proximity to the Arabian Sea exposes Pakistan to the risk of rising sea levels and increased intensity of cyclones and storms, further emphasizing the urgency for effective climate change measures (Khan & Shamim, 2022).…”

Section: Introduction and Background Of The Studymentioning

confidence: 99%

Assessment of School Teacher's Disaster Preparedness by Using Comprehensive School Safety Framework

Iqbal,

Nauman

2024

QJSS

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The Comprehensive School Safety Framework (CSSF) is a comprehensive strategy for a safe learning environment for teachers, students and school staff. This study assessed the disaster preparedness level of secondary school teachers in the public school district Attock. Three hundred twenty teachers were selected from district Attock public based on their age, gender, qualification, teaching level and teaching experience. A five-point Likert scale survey instrument using the CSS framework was used to analyze the disaster preparedness of secondary school teachers. The data was collected through Google Forms using a survey. Data was standardized on MS Excel for a smooth run on SPPS. The data revealed that the majority of the schools are prepared for disaster. There are safe learning facilities, school disaster management, disaster risk reduction and resilience education. However, no school was found to be highly prepared. At the end of the study, the researcher gives further research recommendations on school safety based on the CSS framework.

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Section: Introduction and Background Of The Studymentioning

confidence: 99%

Assessment of School Teacher's Disaster Preparedness by Using Comprehensive School Safety Framework

Iqbal,

Nauman

2024

QJSS

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“…Disasters are multifaceted occurrences that stem from a variety of elements that affect a community's susceptibility and exposure, as well as man-made, natural, and technological threats. (N. Khan & Shamim, 2022) These natural disasters create lots of mess, especially in regions with undulating topography, such as the mountainous region. Landslides are among the most destructive natural hazards, resulting in widespread devastation, including damage to natural resources, fatalities, and property destruction (Mondini et al) Due to the extreme instability of the landmasses over the slope, landslides frequently occur in mountainous places across the world (Conforti et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

Alam,

Siddiqui,

Shamim

et al. 2024

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Mapping landslide susceptibility is crucial for defining high-risk zones and preventing property and human casualties. The Uttarakhand provision, which comes under the Himalayan region, has a high potential for landslide occurrence. A landslide susceptibility map was created using satellite imagery, in-depth field research, and aerial photos. The historical landslide inventory of the state's 14698 total landslides was randomly bifurcated into 70% (10289) for training purposes and 30% (4409) for data validation. Eleven landslide-causative factors (Slope, Aspect, Curvature, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Geology, Normalized Difference Vegetation Index (NDVI), Distance to Road, Distance to Stream, Distance to Fault, and Rainfall) were selected for susceptibility assessment. The landslide susceptibility zonation was created using the Shannon Entropy (SE), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) techniques, along with the causative factors. The AHP method is effectively utilized in LSM to prioritize and weigh the importance of different causative factors contributing to landslide occurrence, while Shannon Entropy uses the method of discrete probability distribution to quantify the uncertainty or variability associated with different causative factors. The FR, AHP, and SE models were validated using the AUC curve, yielding 92%, 89%, and 81% success rates and predictive rates of 90%, 87%, and 77%, respectively. The FR model is most suitable, more efficient, and valuable for future planning in the study area.

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Building Resilience and Management of Vulnerability: Solution for Reduction of Risk of Disasters

Cited by 2 publications

References 27 publications

Assessment of School Teacher's Disaster Preparedness by Using Comprehensive School Safety Framework

Assessment of School Teacher's Disaster Preparedness by Using Comprehensive School Safety Framework

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

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