Significant Natural Disasters Research Articles

Bangladesh's Climate Vulnerability and Overseas Migration: Navigating Challenges and Paving the Way Forward

Bangladesh ranks seventh on the Global Climate Risk Index 2021, which identifies the nations most susceptible to natural catastrophes, demonstrating how sensitive the country is to both climate change and natural disasters. Millions of people are forced to migrate to neighboring cities and urban areas in search of work and a living each year as a result of climate change and natural disasters such as river bank erosion, salinity intrusion, floods, cyclones, and landslides in Bhola, Khulna, Bagerhat, Patuakhali, and Gaibandha. Urban life is strained by this influx, especially in terms of housing, water supply, and sanitation. Many of these individuals also seek overseas employment as a means of survival. While numerous organizations, including government institutions, are working to promote safe and regular international migration and overseas employment, a portion of these individuals fall prey to labor trafficking due to a lack of information, monitoring, and services from the administration. Additionally, communities in climate-risk areas are ultimately forced to choose between internal and international migration due to the sluggish adoption of climate-resilient agricultural methods and technologies, as well as the dearth of technical and vocational education options. For these regions of significant natural disasters and climate change, the government has to provide more secure migration policies and encourage productive remittance investment.

Assessing the Vulnerability and Adaptation Needs of Mozambique’s Health Sector to Climate: A Comprehensive Study

Climate change poses severe consequences, particularly in sub-Saharan Africa, where poverty rates may escalate by 2050 without significant climate and development action. The health impacts are diverse, encompassing communicable and non-communicable diseases. Mozambique, a climate-vulnerable nation, has experienced significant natural disasters in the past 42 years, impacting its health system. This study aims to assess Mozambique’s health sector’s vulnerability and adaptation needs to climate change. Following a methodology proposed by the World Health Organization and the Intergovernmental Panel for Climate Change, a six-step vulnerability and adaptation assessment was conducted to conduct the Health Vulnerability Index (HVI) for Mozambique’s regions (n=161). The HVI integrates historical climate, epidemiological, and socio-economic data at the district level, and was computed using exposure, sensitivity, and adaptive capacity dimensions. The results revealed spatial patterns in exposure to climate variables, extreme weather events, and variations in sensitivity and adaptive capacity across the country. The HVI mirrored the exposure findings. Notably, high vulnerability was observed in several districts, while major urban centers displayed lower vulnerability. These findings highlight the country’s vulnerability to climate change and underscore the potential for adverse impacts on livelihoods, the economy, and human health. The study provides a foundation for developing strategies and adaptation actions.

Assessment of flood risk in Söğütlü stream watershed of Trabzon province in Turkey using geographic information systems and analytic hierarchy process approach

AbstractFloods are significant natural disasters that have wide-ranging social, economic, and environmental consequences on a global scale. They lead to land degradation and increased erosion, which in turn negatively impacts water quality. In the present study, the Söğütlü stream watershed in the Eastern Black Sea Region of Turkey was chosen as the study area to create a flood risk map using Analytical Hierarchy Process and Weighted Overlay tools in the ArcGIS. The raster maps, including soil type, rainfall, landuse, slope, aspect, elevation, and distance to the stream, were utilized as the foundation for the analysis. The flood risk map generated from this analysis reveals five risk zones that is very high, high, moderate, low, and very low-risk zones. Statistical analysis was performed to evaluate the obtained data. The study results indicate that low and moderate areas encompass 95.98% of the watershed, while high-risk and very high-risk areas account for 4.02% of the watershed in the flood risk map of the Söğütlü watershed. It was observed that areas in close proximity to rivers, low slope end elevation were more susceptible to flooding. This study is great importance as it allows for the development of preventive, preparatory, and post-disaster plans before flood events occur by providing a flood risk map. Moreover, the creation of a flood risk map at the watershed scale helps planners in undertaking soil and water conservation efforts during watershed planning.

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry.

A policy analysis on the inclusion of disabilities in natural disaster management in Indonesia

Climate change increases the occurrence of natural disasters. From 1909-2023, Indonesia experienced 613 natural disasters. The flood, which had 268 incidents, was the most significant natural disaster. Indonesia is known for being one of the world’s flood-prone areas. This is due to tidal water inundation, sea level rise, river overflows due to high rainfall, and land subsidence and urbanization. In facing climate change and dealing with natural disasters, no one must be left behind. Indonesia has had a policy for managing natural disasters since 1961, but Indonesia only had natural disaster management related to involving people with disabilities in 2007. This research aims to analyses the process of involving people with disabilities in managing natural disasters. The research uses qualitative methods with content analysis policy. The study finds Indonesia already has a national legal basis for involving disabilities from the planning, implementation, monitoring and evaluation stages as well as in the Disaster Risk Reduction Forum. This study concludes that there is a need for integrity between the central government and regional governments in managing natural disasters with the involvement of disabilities.

A comprehensive assessment model for livelihood vulnerability using fuzzy TOPSIS technique: A case of 2022 flood in Pakistan

One of the most significant natural disasters that results in significant economic losses and human misery is flooding. Flooding can threaten people’s livelihoods because it damages homes, infrastructure, and other essential assets. This study’s objective is to evaluate the livelihood vulnerability (LHV) of those who have been relocated and displaced as a result of the recent flood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The study was conducted in three districts of KP province, which are Charsadda, Nowshera, and Dera Ismail Khan (D.I. Khan). First, a comprehensive literature review was conducted to identify relevant criteria for LHV assessment. As a result, fourteen criteria were selected to measure LHV in the selected regions. Then, a group of experts was invited to prioritize the criteria and provide linguistic ratings for the alternatives in terms of the selected criteria. Finally, the fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) method was used to classify the targeted districts on the basis of their closeness coefficient (CC) values. To assess the robustness of the proposed model, a sensitivity analysis was also provided. The findings of the study revealed that communities residing in D.I. Khan district have the highest LHV compared to Charsadda and Nowshera districts. Besides, loss of harvested or stored crops, total workability, arable landholding, and medical and living expenditures were found to be the most dominant criteria for LHV evaluation. The study concluded that greater efforts are needed during the transition period to improve migrant workers’ employment skills and help them reestablish sustainable sources of income.

Frequency ratio analysis to determine the landslide susceptibility in East Sikkim district of Indian Himalayan region

Landslides are a significant natural disaster causing damage to many mountainous regions worldwide including the Indian Himalayan region. In the East Sikkim district of the Eastern Himalayas, the most used bivariate frequency ratio (FR) model was utilized with high-resolution satellite imagery to understand the susceptibility of the region to landslides. Conditioning factors such as slope aspect, slope angle, slope curvature, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), lithology, and geomorphology were considered in the analysis. LULC is the most crucial factor contributing to landslide susceptibility with a normalized FR value of 14.1. Slope and geomorphology followed closely with values of 12.5 and 11.8 respectively. In contrast, the least important factors were slope aspect and lithology with values of 8.7 and 9.3 respectively. These results can be used to prioritize landslide conditioning factors (LCF) and generate a final landslide susceptibility map (LSM). By adding the values of all LCFs, a landslide susceptibility index was obtained, and the LSM was zoned into high, medium, and low susceptibility classes covering 23.4%, 44.4%, and 32.2% of the study area respectively. The validity of the method used was confirmed using a receiver operating characteristic curve which yielded an accuracy of 78%. The findings highlight the importance of LULC, slope, and geomorphology as critical factors in landslide susceptibility in the East Sikkim district of the Eastern Himalayas.

An Efficient Model for Forest Fire Detection using Deep Convolutional Neural Networks

Forest fires are a significant natural disaster that causes extensive damage to both human and wildlife habitats. Early detection and management of forest fires are critical in preventing potential losses. In recent years, deep learning-based approaches have emerged as promising solutions for forest fire detection. This paper proposes a deep learning-based approach for forest fire detection using SqueezeNet model.The proposed approach utilizes still images captured from forest areas under different weather conditions to classify whether an image contains a fire or not. The models were trained and tested using accuracy, precision, and recall metrics. The experimental results show that SqueezeNet achieve high precision, and recall in detecting forest fires.SqueezeNet is a Convolutional Neural Networks (CNN) architecture designed to reduce the number of parameters and computations required in a deep learning model while maintaining high accuracy in image classification tasks..

FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction

Wildfires represent a significant natural disaster with the potential to inflict widespread damage on both ecosystems and property. In recent years, there has been a growing interest in leveraging deep learning (DL) techniques for predicting the spread of wildfires (WS). However, existing studies have predominantly employed combined features with uniform weighting, overlooking the varying temporal resolutions they can offer (hourly, daily, and constant).As such, this study proposes a hybrid multi-temporal convolutional neural network (CNN) model called FirePred to fill this knowledge gap. In particular, 177 wildfire events were utilized along with related environmental variables between the years 2002 and 2018 in British Columbia, Canada. In pursuit of optimizing the model's performance, an exhaustive exploration of parameter configurations and settings was conducted. This involved assessing diverse combinations of loss functions, padding sizes, batch sizes, and thresholds. Notably, this rigorous analysis yielded an exceptional F1-score of 94% utilizing the most effective parameter set. In addition, to examine the versatility of our proposed model, we conducted an assessment using a dataset encompassing 10 instances of wildfires that transpired in Alaska between 2016 and 2019, as well as a wildfire occurrence in Nova Scotia during 2023. The findings revealed that the performance of the model can be influenced by regional parameters. Finally, the implementation of an uncertainty protocol discovered that the edges of the wildfire contribute the most to the uncertainty.

Study on Spontaneous Combustion Law and Prevention Technology of Abandoned Coal in Goaf of Coal Mine

The spontaneous combustion of coal in goaf areas is a significant natural disaster encountered during coal mining operations. To investigate the oxygen consumption characteristics of different coal types, we conducted TG experiments and closed oxygen consumption experiments using seven coal samples exhibiting various degrees of metamorphism. The findings revealed the following key observations: During the oxidation combustion process of coal, the absorbed heat originates from the physical and chemical adsorption of coal and oxygen. The first exothermic peak on the DSC curve represents the maximum combustion temperature of volatile matter and signifies the initiation of coal burning. Coal with higher ignition temperatures is less prone to spontaneous combustion. Analyzing the thermogravimetric curves, we observed that the ignition temperatures of 15# coal and 8+9# coal predominantly extracted from Duanwang Coal Mine are higher compared to the ignition temperature of the 4# coal seam (known for its susceptibility to spontaneous combustion) in Jiudaoling Coal Mine, Liaoning. This observation aligns with their nonspontaneous combustion characteristics. In the closed oxygen consumption experiment, we determined the volume oxygen consumption attenuation coefficients for the main mining areas’ 8+9# and 15# coal seams as 3.9001 × 10 − 4 and 3.83559 × 10 − 4 , respectively. Additionally, the critical oxygen consumption concentrations were measured as 15.71% and 16.78%. The shortest spontaneous combustion periods were determined to be 119.24 days and 121.25 days, respectively, for the aforementioned coal seams. To mitigate spontaneous combustion, the addition of inhibitors is necessary. The selection of an appropriate inhibitor depends on the increase in ignition activation energy. Notably, coal samples treated with a 15% MgCl2 inhibitor exhibited the highest increase in ignition activation energy. Hence, we concluded that a 15% MgCl2 inhibitor is the most suitable ratio for preventing spontaneous combustion.

Flow–Solid Coupling Analysis of Ice–Concrete Collision Nonlinear Problems in the Yellow River Basin

Yellow River ice is the most prominent and significant natural disaster in winter and spring in China. During the drift ice period, water transmission tunnels located in this area tend to be hit by water–drift ice coupling. Thus, it is an important issue to reduce water transmission tunnel damage by drift ice, ensure the safety of operation and maintenance, and prevent engineering failure. In this paper, a numerical simulation of the collision process between ice and the tunnel is carried out by using the fluid structure coupling method and ANSYS/LS-DYNA finite element software. In addition, a model test with a geometric scale of 1:10 is carried out to verify the numerical simulation results, and the mechanical properties and damage mechanism of drift ice impacting the tunnel concrete lining in water medium are studied. The results show the following: the experimental values of maximum equivalent stress and X-directional displacement of the flow ice on the water transfer tunnel have the same trend as the simulated values, both of which show an increasing trend with an increase in flow ice velocity. It is shown that the ice material model parameters, ALE algorithm, and grid size used in this paper are able to simulate the impact of drift ice on the water transfer tunnel more accurately. With an increase in drift ice collision angle and drift ice size, the fitted curves of equivalent stress and peak displacement in X-direction all show relationships of exponential function. The peak value of displacement in the X-direction and maximum equivalent stress decrease with an increase in the curvature of the tunnel structure. It is also shown that the influence of change in drift ice size on the tunnel lining is greater than that of a change in tunnel section form. It is found that a high-pressure field will be formed due to extrusion of flowing ice, which should be fully considered in the numerical simulation. The research method and results can provide technical reference and theoretical support for prevention and control of ice jam disasters in the Yellow River Basin.

Radar Reflectivity of Micro Rain Radar (MRR2) At 16.44180N, 80.620E of India

To improve accurate standards of Radar Reflectivity Z, Rainfall Rates RR, and even to monitor small size precipitation particles Z-R relation is derived in this work with the help of Micro Rain Radar. Formerly, taking rain/precipitation data from ground-based rain gauges (Cylindrical, Optical, Weighing, Tipping Bucket Rain Gauges, Disdrometers, etc.,) currently, in this work using METEK MRR (Micro Rain Radar) of Frequency Modulated Continuous Wave System which reads the vertical structure of precipitation particles and hence named as vertical profile radar which operates at 24.2 GHz and height up to 6000m/6kms with an increment step size of 200m respectively to monitor the frozen hydrometeors. It is installed at (16.440 N, 80.620 E) K L University, 29 meters above the sea level (ASL) in CARE LAB. The METEK is manufacturer of micro rain radar, to know the Radar Reflectivity of precipitation, it is observing the amount of power received to the radar receiver after hitting the precipitation particles with respect to the transmitted power of the radar. This proposed work considered distinctive rain conditions at different heights ASL ranging from 200m 1200m 2200m, and derived Z-R relations respectively. This work has been provided significant natural disasters such as Geophysical, Hydrological, Climatological and Meteorological pre-intimation easily using METEK MRR.

Flash Flood Hazard Mapping Using Remote Sensing and GIS Techniques in Southwestern Saudi Arabia

Flash flooding is one of the most significant natural disasters in arid/hyperarid regions and causes vast property damage and a large number of deaths. For mitigating and reducing flood risks, data from several remote sensing satellite images—Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Landsat 8 Operational Land Imager (OLI), and Tropical Rainfall Measuring Mission (TRMM)—were prepared and combined through a GIS-based multicriteria decision-making technique to test and delineate the flash flood vulnerable areas of Wadi Hali in southwestern Saudi Arabia. Several flash flood thematic layers representing topographic, geomorphic, climatic, and hydrological conditions were prepared, normalized, and combined through a GIS- based analytic hierarchy process (AHP) technique to obtain flash flood hazard zones (FFHs). This method successfully presented a satisfactory output map that revealed six zones of flood risk, and areas of extreme hazard covered about 13% of the entire basin. Landsat 8 band composite 7, 5, and 3 and field data validated the FFHs. This map considered a key requirement for sustaining safe settlements downstream of Wadi Hali. Overall, the integration of remote sensing and GIS techniques revealed significant areas of flash flood zones in an arid region.

Application of Rainfall Threshold for Sediment-Related Disasters in Malaysia: Status, Issues and Challenges

Sediment-related disaster is one of the most significant natural disasters, from the perspective of magnitude, damage and loss to human life and infrastructure, and disruption to socio-economic activities. Debris, mud flood, landslide and cliff failure are the major catastrophic problems commonly experienced in most developing countries, including Malaysia. As rainfall is the main culprit to sediment-related disaster occurrences, rainfall data are crucial in the correlation of the occurred events. Several studies have been undertaken worldwide to estimate the critical rainfall conditions and draw the benchmark to predict landslide occurrences, specifically for debris and mudflows (DMF), and shallow landslides. Therefore, this paper presents an up-to-date picture on the development of the rainfall threshold from Malaysia’s perspective. Additionally, the open issues and challenges of deriving the rain threshold are also discussed in three aspects: collection of the dataset features, identification of the threshold and validation of the threshold. The outcomes of this review could serve as references for future studies in Malaysia and other developing countries in managing sediment-related disasters.

Drought Class Probability Analysis for the Küçük Menderes River Basin

Drought is a complex natural hazard that may take many years to develop and that has significant effects on human life, environmental systems, and the economy. Its impacts may also be serious and costly. This study, considering the fact that drought is a significant natural disaster, aimed to investigate the characteristics of drought in the Küçük Menderes River Basin. Among several drought indices, the Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) were used for drought analysis. SPI and SDI were applied to the monthly precipitation data of Selçuk and Ödemis meteorological stations with nos. 17854 and 17822, and to the monthly flow data of Selçuk and Bülbüller flow gauging stations with nos. E06A001 and D06A012, respectively. Drought classes for each month were obtained through the referred drought indices. Using the drought classes, meteorological and hydrological droughts were statistically investigated at a regional scale by determining the drought classes’ occurrence probabilities, the expected residence time in each drought severity class, the expected first transition time, and the drought class with the highest probability of occurrence in the near future.

The Impact of Significant Events on Public Policy and Institutional Change: Towards a Research Agenda

Data shows that significant events such natural disasters, anthropogenic disasters and malign activities by hostile actors, often having cross-border effects, have been on the rise. However, the studies of the effects of those events on public policies, governance and institutions remain inconclusive. In this article we present a research agenda which proposes the classification of the significant events on the basis of their characteristics backing it with a newly compiled data set on significant events which took place in Lithuania in 2004-2020 and outline the directions for an in-depth analysis of the causal mechanisms of how those events affect policy and institutional change. We conclude with concrete proposals for further research which could provide theoretically innovative and policy relevant insights into the political processes which translate responses to significant events into policy and institutional changes affecting welfare institutions and resilience of society.

HEAT WAVE AND ITS IMPACT IN NORTHEASTERN NIGERIA: A CASE STUDY OF YOBE STATE, NIGERIA

Heatwaves are significant natural disaster that is caused primarily by extreme temperatures. The extreme heat events associated with heatwaves often cause severe health complications and deaths. In view of the environmental hazards associated with heatwaves, this study investigated the impact of heatwaves in Yobe State, northeast Nigeria. Temperature and humidity data of thirty years (1991-2020) for the hot season (March-June), were obtained from the Nigerian Meteorological Agency (NiMet) Abuja.  Time series analysis and basic statistical tools were used to analysis the data which was used to evaluate the trend of heatwaves and their impact in the study area. The results of the study showed that 2010 was extremely hot with an average maximum temperature anomaly of 2.18, while 2013 and 2015 have an average maximum temperature anomaly of 1.93 respectively, and were very hot. The trend in the average maximum temperature anomaly of the study area showed a continuous increase in extreme heat for about a decade (2009-2020). The positive temperature trend and the average maximum temperature anomaly showed that the intensity of heatwaves in the study area will increase in the future as a result of global warming caused by the continuous emission of anthropogenic greenhouse gases. The results also showed that the heatwaves were exacerbated by the drought which occurred between 2010 and 2014.  The annual heat index which range from 29.4oC - 34oC in the study area showed that the heatwaves have impacted both human and animals negatively in the area.

The religious fringe of Corporate Social Responsibility

In this paper, we explore how the adherence to Sharia principles in non-financial firms impacts on Corporate Social Responsibility (CSR) aptitudes, proxied by Environmental, Social and Governance (ESG) scores. By employing a worldwide dataset of 4,725 entities from 2002 to 2018, we find that Sharia-compliant firms are more likely to engage in sustainable and responsible practices, especially towards the environmental dimension. Evidence from a set of quasi-natural experiments, built around a range of significant natural disasters and social events, reveals a greater response of Sharia firms to CSR engagement, confirming the role played by faith-based business ethics in Islamic entities. Our results offer precious insight to policymakers and investors, suggesting that the integration of ethical and religious-oriented incentives can improve sustainability practices and resilience of firms.

AN APPLICATION OF RAINFALL THRESHOLD FOR SEDIMENT RELATED DISASTER IN MALAYSIA, ISSUES AND CHALLENGES

Sediment related disaster is the most significant natural disaster in Malaysia and many countries in this world from the perspective of magnitude, damage and loss to human life and infrastructure as well as disruption to socio-economic activities. Debris, mud flood, landslide and cliff failure are some of the major catastrophic problems and became a history for the country especially Cameron Highlands in a state of Pahang. As rainfall is the main culprit to sediment-related disaster occurrences, therefore the rainfall data is very crucial to be used in the correlation of the occurred events. Due to that fact, several studies worldwide have been made to estimate critical rainfall conditions and this being useful to draw the benchmark to predict the occurrences of the landslide specifically for DMF and shallow landslides. This paper discussed the development of the rainfall threshold in Malaysia by compiling the framework of the threshold to determine the lesson learned as well as the way forward. As Malaysia needs to move at a faster pace towards embracing the whole aspects in determining the threshold as well to implement it into the operational threshold, therefore the first step is very important to initiate the momentum while the collaboration or networking among government agencies in National Disaster Risk Reduction (DDR) should be enhanced and strengthened.

Design of an Atlas of Significant Natural Disasters

Abstract. Natural disasters change every year, which leads to anomalies and subsequently causing more and more damage. Therefore, it is important that people receive as much information for disasters as possible, in order to be aware of their power and consequences. The clearest way to transmit information about a disaster, before, during and after its occurrence, is to map it. This paper discusses the applicability of atlas mapping, and the process of creating an “Atlas of significant natural disasters, around the world, for 2019”. The Atlas collects, and consolidates historical and up-to-date information on natural disasters around the globe, including tropical cyclones, earthquakes, floods, pandemics and wild fires. By combining this information with data on population and infrastructure, the Atlas provides users with a better understanding of current hazards and their potential impacts.