Potential Disaster Research Articles

Brief communication: The northwest Himalaya towns slipping towards potential disaster

Abstract. The NW Himalaya (India) has been one of the most affected terrains of the Himalaya, subject to frequent disastrous landslides owing to active tectonics and multiple precipitation sources. This article focuses on two towns (Joshimath and Bhatwari) of Uttarakhand in the NW Himalaya, which have been witnessing subsidence for decades. Up until 9 January 2023, Joshimath had witnessed widespread cracks in more than 500 houses, which has prompted social unrest in the region. The hillslopes surrounding both towns comprise highly jointed gneisses with schistose interlayer rock mass. Both towns have subsidences and holes in the road, broken retaining walls, and displaced boulders, and the houses have cracks in the walls and holes. Recently, such slope instability phenomena have increased, which is leading to social movements in the region seeking government action such as possible evacuation and rehabilitation. The present study used a continuum-modelling-based slope stability simulation to determine the response of these hillslopes under various loading conditions: gravity, rainfall, building load, domestic discharge, and seismic load. Results revealed that the displacement in these hillslopes might reach up to 20–25 m, which will further aggravate the situation. The occurrence of frequent extreme rainfall in these towns and three major earthquakes, which occurred on 1 September 1803 (Mw 7.8), 20 October 1991 (Mw 6.8), and 29 March 1999 (Mw 6.6), having a hypocentral distance less than 30 km makes such a study more viable for decision making.

Inflow Prediction of Centralized Reservoir for the Operation of Pump Station in Urban Drainage Systems Using Improved Multilayer Perceptron Using Existing Optimizers Combined with Metaheuristic Optimization Algorithms

Owing to the recent increase in abnormal climate, various structural measures including structural and non-structural approaches have been proposed for the prevention of potential water disasters. As a non-structural measure, fast and safe drainage is an essential preemptive operation of a drainage facility, including a centralized reservoir (CRs). To achieve such a preemptive operation, it is necessary to predict the inflow of the drainage facilities. Among the drainage facilities, CRs are located downstream of the drainage area, and their pump stations are operated according to the CR water level. The water level of a CR depends on the inflow, as does the preemptive operation of its pump station. In this study, as a nonstructural measure, the inflow prediction for the CR operation in an urban drainage system was proposed. For predicting the inflow of a CR, a new multilayer perceptron (MLP) using existing optimizers combined with a self-adaptive metaheuristic optimization algorithm, such as an improved harmony search, was proposed. Compared with the adaptive moment, which yields the best results among other existing optimizers, an MLP using an existing optimizer combined with an improved harmony search improves the mean square error and mean absolute error by 0.1767 and 0.0349, respectively.

A methodological framework for ranking communicable and non-communicable diseases due to climate change – A focus on Ireland

There is currently a significant global focus from the public health community on addressing climate-related public health issues. Globally we are witnessing geological shifts, extreme weather events, and the associated incidents that may have a significant human health impact. These include unseasonable weather, heavy rainfall, global sea-level rise and flooding, droughts, tornados, hurricanes, and wildfires. Climate change can have a direct and indirect health impact. The global challenge of climate change requires global preparedness for potential human health effects due to climate change, including vigilance for diseases carried by vectors, foodborne and waterborne diseases, deteriorated air quality, heat stress, mental health, and potential disasters. Therefore, it is essential to identify and prioritise the consequences of climate change to become future-ready. This proposed methodological framework aimed to develop an innovative modelling method using the ‘Disability-Adjusted Life Year (DALY)’, to rank potential direct and indirect human health impacts (communicable and non-communicable diseases) of climate change. This approach aims to ensure food safety, including water, in the wake of climate change. The novelty of the research will come from developing models with spatial mapping (Geographic Information System or GIS), which will also consider the influence of climatic variables, geographical differences in exposure and vulnerability and regulatory control on feed/food quality and abundance, range, growth, and survival of selected microorganisms. In addition, the outcome will identify and assess emerging modelling techniques and computational-efficient tools to overcome current limitations in climate change research on human health and food safety and to understand uncertainty propagation using the Monte Carlo simulation method for future climate change scenarios. It is envisaged that this research work will contribute significantly to developing a lasting network and critical mass on a national scale. It will also provide a template to implement from a core centre of excellence in other jurisdictions.

Analysis of the Implementation Rice Farming Insurance Program based on the Mapping of Rice Fields with High Flood Risk in Grobogan Regency

Central Java Province in 2021 was the second largest rice producer in Indonesia, with Grobogan Regency as the largest producer in Central Java. It was chosen as the object of research because it is the largest producer in this province with a high level of vulnerability to crop failure. The highest factor of crop failure in Grobogan Regency is the flood disaster. Although they always face the risk of crop failure, the participation of farmers in this regency in rice farming insurance program is relatively low. From the spatial data of rice fields and potential flood disasters, it was found that Godong District is the area with the highest potential for crop failure in Grobogan District, followed by Brati District and Karangrayung District. The flood risk data paired with the participation data of the program for the last four years showed that three districts possess high potential for flood disasters, but the participation of the program is still low. The percentage of participation based on rice field area are Godong District 1.73%, Brati District 0.5%, and Karangrayung District with 0%. The interviews with farmers in the three districts found that most farmers were still not aware of the potential for flooding that could threaten their land, supported by the stigma of the insurance program in general, and public figures in the village who had not yet set an example for participating in this program.

Career Decisions and Opportunity Recognition as The Predictors of Entrepreneurial Intention

The process of entrepreneurship has a significant reputation for the career-related choice gained by innovation and risk-taking. The present study proposes to investigate the career decisions, opportunity recognition (ORN) and entrepreneurial intention (EIN) levels among university students studying sports sciences in Pakistan. The present study explores the EIN of young adults greatly associated to their career decisions and recognizes opportunism. The study employed cross-sectional data collected throughout Pakistan using a survey questionnaire. In total, 282 valid samples are utilized for concluding the investigation. By applying the path analysis through AMOS, the findings suggest a positive and significant effect of career decisions [(conscious career decisions (CCD) and blind career decisions BCD] on EIN among sports science students. Irrespective of this, environmental factors (ENFs) have an insignificant impact on EIN. The outcomes of a research study may provide guidelines for policymakers and universities’ authorities to accomplish and support the career development of students and accomplish the potential career catastrophes relating to the significant facts of unemployment issues and unemployed young adults. Moreover, the study would also assist the students in recognizing their career opportunities to tackle the unemployment trends. Further, the effect of ORN on EIN is also found to be positive and significant.

PERAN BADAN PENANGGULANGAN BENCANA DAERAH (BPBD) DALAM MENJALANKAN PROGRAM SATUAN PENDIDIKAN AMAN BENCANA (STUDI KASUS BPBD KABUPATEN BANTUL)

This article has a focus on the role of the Regional Disaster Management Agency in schools in carrying out the Disaster Safe Education Unit (SPAB) program with the case study BPBD Bantul Regency. This is because Indonesia is a country with a high level of natural disasters because it is at the confluence of various earth plates. Meanwhile school is the second place for students to seek and gain knowledge after being at home, especially in studying potential disasters that can occur in the environment where they live. It should be noted that in 2006 an earthquake with a magnitude of 5.9 occurred in Bantul Regency which resulted in fatalities and heavy losses, including educational facilities. The purpose of this study is to find out how the role of BPBD Bantul Regency in disaster management in schools so far and to know and see how the Disaster Safe Education Unit program in Bantul Regency is running. The method used in this research is descriptive qualitative. The results obtained are that the BPBD within the scope of the school plays a role in educating children to have a more disaster preparedness attitude, helping facilitate school needs, and introducing disaster education from an early age. The SPAB program is carried out to build resilience in dealing with disasters by school members, one of whose efforts is to provide material about disasters. BPBD encourages schools in Bantul Regency to incorporate disaster material into their learning and extracurricular.

Waterline digital elevation model development to quantify inundation duration and coastal protection of tidal wetlands

Coastal tidal wetlands are sufficiently acknowledged for the supplied vital ecosystem functions, including flood protection and biological conservation. Measuring and estimating reliable topographic data is essential for quantifying mangrove habitat quality. This study proposes a novel methodology for quickly constructing a digital elevation model (DEM) with an instantaneous waterline combined with tidal level records. Unmanned aerial vehicles (UAVs) enabled on-site waterline interpretation analysis. The results show that image enhancement improves the accuracy of waterline recognition and object-based image analysis has the highest accuracy. The waterline DEM (WDEM) performs a more accurate elevation production than UAV DEM, indicating that its application to habitat evaluation and prediction could be more reliable. Hydrodynamic simulations incorporated with the mangrove habitat model were utilized to calculate inundation duration, flow resistance, and vegetation dissipation potential according to the verified WDEM. The larger the mangrove coverage ratio, the stronger the flow resistance, which means that the protective consequence of the mangrove on the natural embankment is evident. The WDEM and nature-based solutions presented facilitate an adequate understanding of coastal protection and promote the potential ecosystem-based disaster risk reduction of mangrove wetlands.

Assessment of future flood risk induced by sea level rise and tropical cyclones under global warming in the Xiamen Bay, Fujian, China

Tropical cyclone (TC)-induced coastal flooding can lead to severe hazards in low-lying lands and is expected to be exacerbated by sea level rise and TC climatology changes related to the warming climate. Since the dense population in Xiamen Bay, Fujian, China, it is highly valuable in understanding coastal flooding of it, but little studies involved this topic. In this study, we establish a high-resolution numerical model covering all coastal low-lying land in Xiamen Bay based on FVCOM. This model is then used to assess the flood risk in Xiamen Bay under TCs with 10-, 100-, and 1000-year pressures by applying a multi-tracks blend method. The results show that 126 km2 of low-lying area can be flooded periodically by astronomical tides; and 388, 504, and 598 km2 of low-lying area can be flooded under TCs with 10-, 100-, and 1000-year pressures, accompanied by increased average flood depths of 2.3, 2.8, and 3.4 m, respectively. By 2100 under SSP5-8.5, the well-protected Xiamen Island becomes impacted by TCs with 10-year pressure, and flood areas under TCs with 100-year pressure are estimated to be nearly equivalent to that under TCs with 1000-year pressure at the current climate. The most increased exposure area by climate change are artificial surfaces and agricultural areas, showing the potential higher flood disasters in future.

A Geo-Hazard Risk Assessment Technique for Analyzing Impacts of Surface Subsidence within Onyeama Mine, South East Nigeria

This paper proposes a geo-hazard risk assessment technique to analyze the impacts of surface subsidence monitored in a major coal mine in Nigeria. In many developing countries, disaster risk management schemes have mainly focused on traditional singular hazard assessment, vulnerability assessment, or risk assessment. However, it is difficult to use a singular application to adequately address hazard assessment due to the variation in data requirements, factors associated with the hazards, and the various elements at risk. Most times, hazard assessment schemes heavily rely on data and techniques from different global organizations that collate data on disasters, using various scales and objectives to make informed decisions. Several challenges seemingly arise from total reliance on these kinds of data due to standardization, the exact number of potential victims, and the purpose of the data collection. This makes disaster information collected at the local level unique and assessment schemes more complete; however, the coverage is limited worldwide. The proposed approach combines the spatial relationship between vulnerability assessment and elements at risk to highlight the grave consequences of potential disasters. Thus, the aim is to underscore the importance of integrating local-level inputs in analyzing risk factors and vulnerability indicators for hazard assessment. This study was conducted at the Onyeama coal mine in South East Nigeria. This area has experienced severe negative impacts of subsidence over the years. We exploit data from Sentinel-1 Synthetic Aperture Radar (SAR) Satellites and Small-Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) technique to map the study area. The results generate an elements-at-risk database with a particular focus on population density, road networks, and building networks identified as indices for loss estimation.

The community participation on infrastructures and facilities updating data of Teratak Village, Rumbio Jaya

Village Information System (SID) is an implementation of e-government by the village government. The right of village communities to get real-time information can be hampered due to the skills and abilities of human resources and out-of-date information. The activity is focused on updating Village data and facilities through survey primary data collection. The results of the survey are up-to-date data, are integrated, and can be accessed online. The process of entering data into the SID will be continued by the Village Government Officials who manage the SID, for this reason, there is a need for a special briefing for Village Government Officials in a training activity in updating data on village facilities and infrastructure. Workshops in the form of training, practice and question and answer were carried out with feedback in the form of questionnaires before and after the workshop to assess the success of the program. The survey data that is input to the SID includes health facilities, religious facilities, agriculture, animal husbandry, plantations, educational facilities, village tourism potential, and potential disasters along with their evacuation points. This updated data is expected to be a reference for compiling the Teratak Village Development and workshop activities have been able to improve the ability of village government.

An Unmanned Aerial Vehicle (UAV) System for Disaster and Crisis Management in Smart Cities

Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the use of UAVs in disaster and crisis management. In the event of a catastrophic event, it would be extremely beneficial for both victims and first responders to have access to a UAV network that is capable of deploying independently and offering communication services. However, when working with complicated situations, one of the most difficult things is coming up with exploratory paths for the networks involved. A crisis and disaster management system using a swarm optimization algorithm (SOA) is proposed to assist in disaster and crisis management. In this system, the UAV search and rescue team follows the strategy called the delay tolerant network, which has the ability to explore. The proposed approach is able to find the global maximum in the search space without ever settling for a suboptimal solution. This work has two primary objectives: the first is to investigate a potential disaster zone, and the second is to direct the UAV to a number of victim groups that were found during the investigation phase. For the purpose of performing a characterization, performance metrics such as delay, throughput, performance rate, and path loss have been analyzed. The results show the superiority of the performance over the existing work.

AHP Scoring and Weighting Main Criteria For Assessment of Potential Hydrometeorological Disasters: A Literature Study

In general, the territory of Indonesia is prone to hydrometeorological disaster events. In disaster management, it is necessary to map the source or level of potential disaster hazards. The Analytic Hierarchy Process (AHP) method has been used to determine the relative importance of each criterion for potential mapping disasters. This literature study aims to determine the criteria and weighting in the AHP approach that can be used in assessing the potential for hydrometeorological disasters. This literature review is limited by analyzing and assessing the potential, vulnerability, and vulnerability of floods, landslides, and tornadoes using the AHP method. The input data used in the literature is based on the opinions of experts, interested stakeholders, and related regulations to determine the criteria and their weighting. The results of this literature review show that the three criteria are the dominant determining factors in assessing and analysing the three hydrometeorological disasters. The scoring of each criterion is based on its impact on the disaster.

LPG Leakage Detection and Disaster Prevention System

Liquid petroleum gas (LPG) is a fuel that is commonly used in household and commercial settings, it can be dangerous if not handled appropriately. The goal of the "LPG Leakage detection and disaster prevention system" project is to develop a comprehensive method for detecting LPG gas leaks and preventing potential disasters. The project makes use of a buzzer, a DC motor, a water pump, a MQ2 gas sensor, and a fire sensor, among other parts. To give real-time notifications over the internet via the Telegram API, an IoT application based on the ESP32 microcontroller is also included. The MQ2 gas sensor, which is in charge of detecting the presence of LPG gas, it serves as the system's primary input. When a gas leak becomes apparent, the system activates the buzzer and the DC motor to ward off any LPG gas that may be present in the environment and notify the user of the potential risks. This makes it possible to take immediate measures to reduce the risk of a gas explosion. This project also covers the potential for fire outbreaks unrelated to gas leaks in addition to gas leak detection. A fire sensor is incorporated into the system to address this problem. If a sudden fire is detected, the fire sensor triggers the water pump, representing a sprinkling system designed to extinguish the fire promptly. This approach enhances the safety measures and ensures the protection of life and property. The ESP32 microcontroller is used to create an IoT application that will improve the system's functionality. Through this application, the system can be remotely monitored online. The Telegram API notifies the user's device when there are fires or gas leaks. User’s will have the ability to get real-time notifications even when they are not physically present at the system's location, so disaster prevention measures can be taken in a timely manner.

Reasonable Working-Face Size Based on Full Mining of Overburden Failure

To improve production efficiency and prevent potential disasters in coal mines, comprehensive research methods such as similar simulation, numerical simulation, theoretical analysis, and on-site detection were used in this study. The migration characteristics of overburden rock under different working face lengths and development heights of water-conducting fracture zones were investigated via these methods in order to determine the reasonable length of the working face. The results show that the regularity of the development height of water-conducting fracture zones in similar simulations and numerical simulations is highly consistent, and the final stable values are 48 and 50 m. When the working face length was 300 m, the error between the simulated value and the value calculated from the formula dropped below 10% and exhibited a further decreasing trend; as a result, the working face length of 300 m was found to be the turning point for the development height of the water-conducting fracture zone to become stable. Based on the simulation results and mining damage theory, the critical size of the working face was 307.6 m, and the height of the water-conducting fracture zone was determined to be in the range of 45.5–60.5 m. The actual detection result of the height of the water-conducting fracture zone under the critical size of the working face was 55 m, which conforms to the law obtained from the simulation. Finally, the reasonable working face length under the geological conditions of a coal mine was determined to be 300–400 m. This study offers important reference value for determining the reasonable working face length under similar geological conditions, and may have significance for the sustainable development of coal resource mining.

A deep learning framework for target localization in error-prone environment

The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and early localization of its source is crucial. Target localization is performed with the help of IoT sensors, which are typically deployed in hazardous environments and are not easily serviceable, making energy efficiency a requirement for SEM systems. Furthermore, IoT sensors may provide anomalous readings due to low battery, low sensor sensitivity, faults, or malicious attacks. This alters the decision-making process and leads to inaccurate localization of the source. Hence, detecting and dealing with anomalies, while utilizing a limited number of sensor nodes, are key factors in addressing reliability and energy efficiency issues. The current works are iterative in nature, employs redundant nodes, do not efficiently address anomalous readings, which leads to slow and imprecise localization, while increasing energy consumption. Moreover, they are application specific, making them less adaptable to other sensing tasks. Therefore, this work proposes a comprehensive and novel deep learning-based system that — (a) repopulates missing data from inactive sensors, (b) detects and rectifies anomalous data, and (c) instantly localizes a target. The efficacy of the proposed approach is validated in a radioactive environment and compared to existing benchmark. The results show that the proposed approach achieves precise, rapid, and efficient localization by achieving 5 times better accuracy, 50 times faster speeds, and 3 times lesser energy consumption.

Pore pressure built-up in hydrate-bearing sediments during phase transition: A poromechanical approach

Due to the density contrast between the hydrate and methane gas, the pore pressure is accumulated in the sediment during the decomposition process of methane hydrate. This accumulation of pore pressure decreases the magnitude of effective stress, further triggering potential geological disasters such as landslide. This paper establishes a theoretical framework to investigate the evolution of fluid pressure in the hydrate-bearing sediments during the decomposition process. This model consists of two parts: an unsaturated thermo-poromechanical constitutive law as well as a phase equilibrium equation. Compared with the existing studies, the present work incorporates the effect of pore volume change into the pressure built-up model. In addition, the capillary effect is considered, which plays a nontrivial role in fine-grained sediments. Based on this model, the evolution of fluid pressure is investigated in undrained conditions. It is shown that four mechanisms mainly contribute to the pressure built-up: the density contrast between decomposing hydrate and producing fluid, the variation of pore volume, the compaction of hydrate due to variation of capillary pressure, and the thermal deformation of pore constituents induced by temperature change. Among these mechanisms, the density contrast dominates the pore pressure accumulation. Under the combined effect of these contributions, the evolution of fluid pressure exhibits a strong nonlinearity during the decomposition process and can reach up to dozens of mega Pascal. Nevertheless, this high-level pressure built-up results in a significant tensile strain, yielding potential fracturing of the sediment.

Contingency Planning in Pre-Incident based on Reinforcement Learning Assuming Multiple Faults in Transmission Lines by Typhoons

Efforts to minimize power outage caused by natural disasters receive much attention. In that context, contingency plan in pre-incident is expected to be effective for reducing potential disaster damage and realizing quick recovery. Existing contingency planning schemes are usually designed as model-based approach to prevent either the worst-case scenario or a few typical scenarios. However, the effectiveness and adaptability of these schemes can be reduced in the long-term disaster such as a typhoon; there are an enormous number of risk scenarios caused by cascading accidents over time. In this paper, we propose a policy-based planning approach based on reinforcement learning for pre-allocation of power-supply cars and re-scheduling generator operation. By learning effective policies via simulations of various disaster scenarios in advance, the model can instantly output plans tailored to the disaster situation even for an enormous number of disaster scenarios. Therefore, the derived plan can follow the progress of the disaster dynamically. The practicality of proposed method is evaluated via numerical experiments based on the original grid simulating Eastern Japan and Typhoon No. 15 in 2019. The result shows the reduction of power outage by the proposed method compared to the existing method under multiple faults in transmission lines.

Pendampingan Masyarakat dalam Pencegahan dan Penanggulangan Bencana Banjir di Kecamatan Candisari Kota Semarang

Candisari district is one of the urban villages in Semarang City which is prone to flooding and landslides because of its hilly position and lots of cliffs. The party that loses the most when a disaster occurs is the community, so community participation is needed in preventing floods and landslides. This assistance activity begins with increasing the knowledge of residents regarding potential disasters in the Candisari area. The purpose of this activity is to examine community empowerment in flood disaster prevention and management. Increasing empowerment can be done by applying the community empowerment approach, eliminating the inhibiting factors of the empowerment process from an institutional and community capacity perspective and applying the principles of community empowerment. Recommendations from this activity are aimed at several relevant stakeholders such as facilitators, local government and the people of the Candisari Village who are expected to be able to focus on prevention by consulting regarding infrastructure development in the area.

Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India)

Floods are potential natural disasters that might disrupt human activities, resulting in severe losses of life and property in a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of their complexity, understanding and forecasting rainfalls are challenging. This paper aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting average monthly rainfalls by considering several surface weather parameters as predictors. The Upper Brahmani Basin, which extends over 17,504 km2, was considered as a study area. Therefore, an ANFIS model was developed to forecast rainfalls using 37 years of climate data from 1983 to 2020. A hybrid model with six membership functions provided the best forecast for the area under study. The suggested method blends neural network learning capabilities with transparent language representations of fuzzy systems; 75% of data (from 1983 to 2006) was set aside for training and 25% (from 2006 to 2020) for testing. The Gaussian membership function with the hybrid algorithm provided satisfactory accuracy with R-values for training and testing equal to 0.90 and 0.87, respectively. Therefore, a new promising forecasting model was developed for the period from 2021 to 2030. The highest rainfall was forecasted for the period June–August, which is a striking characteristic of the monsoon climate. The study area is relatively close to the equatorial warm climate region. Hence, the proposed model might be of consistent use for regions lying in similar latitudes.

Risk assessment of marine disasters in fishing ports of Qinhuangdao, China

The data of the Cross-Calibrated Multi-Platform (CCMP) wind field was used as the background wind field to reconstruct the Yellow Sea and Bohai Sea wind fields for 30 years from 1991 to 2020. The observed and reconstructed wind fields were in good alignment. Reconstructed wind-field data were used to drive the SWAN model. In comparison, the calculated significant wave heights and measured data were in good agreement. Based on the SWAN calculation results, the wave elements of the 10-year return period were calculated to provide the input boundary conditions for the MIKE21 SW model. By calculating the wave fields near the fishing ports, the hidden dangers of marine disasters in six fishing ports in Qinhuangdao were analyzed. The results show that there are hidden dangers of marine disasters in the Xinkaikou fishing port; Daihekou, Yanghekou and Shanhaiguan fishing ports all have potential marine disasters risks. In addition, no hidden dangers of marine disasters in the Xieliangkou fishing port and Fishing vessel wharf port were identified.