Disaster Management Applications Research Articles

Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application

Climate change poses several environmental threats like floods to urban environment; thus, effective and reliable communication of emergency information is needed during massive breakdown of network infrastructure. This paper presents a mobile adhoc network (MANETs) based effective information such as calls, image, and videos communication system that is compatible with current 3GPP and 5G communication network. Here in maintaining connectivity the information is communicated between different MANET nodes in a multi-hop manner. However, designing radio propagation is challenging considering higher local emergency request congestion at different terrain with varying speed of users. The current radio propagation model is designed without considering the effect of line-of-sight between communicating device and are not adaptive to different environment considering urban disaster management environment. This paper develops an adaptive radio propagation (ARP) model namely expressway, city and semiurban. Then, in reducing congestion and improving network performance efficiency the work introduced an adaptive medium access control (AMAC) protocol. The MAC incorporates a dynamic network controller (DNC) to optimize the contention window size in dynamic manner according to current traffic demands. The AMAC protocol achieves much improved throughput with lesser packet loss in comparison with existing MAC (EMAC) model considering different radio propagation model introduced in this work.

Efektivitas Aplikasi Sidoarjo Tanggap ‘Sigap’ dalam Penanggulangan Bencana di Kabupaten Sidoarjo

Sidoarjo Regency is one of the areas in East Java Province which has the potential for natural and non-natural disasters. So this disaster needs to be overcome with the role of the Sidoarjo Regency BPBD in efforts to deal with the impact of the disaster. So the Sidoarjo Regency BPBD created the Sidoarjo Response Application 'Sigap' which functions for the community if a disaster occurs or is affected by a disaster in the community. This research uses a qualitative method with a descriptive approach using primary and secondary data obtained through observation, interviews and documentation. The focus of this author's research is the effectiveness of the Sidoarjo Response 'Sigap' application in disaster management in Sidoarjo Regency. The results of this research explain that the Sidoarjo Regency BPBD has carried out its role in efforts to increase the effectiveness of the Sigap Application according to its duties and functions, namely: 1). System Quality 2). Information quality 3). Service Quality. There are obstacles or obstacles to research, namely the activities of village communities, but these obstacles can be minimized through the decision of the District BPBD. Sidoarjo. Supporting Factors are the involvement of all elements of Sidoarjo Regency society in participating in activities provided by agencies related to the Effectiveness of the Sigap Application in Disaster Management in Sidoarjo Regency.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

Smart edge segmentation and localization method for building detection in satellite imagery

The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows.

Fast Disaster Event Detection from Social Media: An Active Learning Method

This study introduces a novel framework for fast disaster event detection from social media,hich incorporates active learning into Bi-directional Long Short-Term Memory (Bi-LSTM). In theace of increasing disaster-related information on social media platforms, our method addresses thehallenge of efficiently processing vast, unstructured datasets to accurately extract crucial disasterrelatednsights. Leveraging the contextual processing strengths of Bi-LSTM and the data efficiencyf active learning, our approach significantly outperforms several baseline models in accuracy,recision, recall, and F1-score. We demonstrate that our method effectively balances these metrics,nsuring reliable disaster detection while minimizing false positives and negatives. The studylso explores the impact of various model parameters on performance, offering insights for futureptimizations. Despite its promising results, the study acknowledges limitations such as datauality, representativeness, and computational resource requirements. Future work will focus onnhancing data preprocessing, expanding language and scenario applicability, and integrating theodel with additional technologies for broader disaster management applications.

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

Accurate storm surge prediction in hurricane area of the Atlantic Ocean using a new multi-recursive neural network based on gate recursive unit

This paper proposes an enhanced storm surge forecasting approach utilizing novel neural networks, specifically a model based on gated recurrent units (GRUs), and incorporates a distortion loss function known as Distortion Loss Including Shape and Time (DILATE) for improved prediction accuracy and reliability. Historical data from four stations in the hurricane-prone region of the Atlantic Ocean are utilized for both training and testing purposes. Various models are employed to predict storm surges with lead times of 3, 6, and 12 hours, and their performance is evaluated by comparing the predicted values with the observed ones. Furthermore, the influence of various physical factors on storm surge formation is examined. The findings indicate that the GRU-DILATE model is more suitable for storm surge prediction, particularly for longer lead times. This model effectively reduces prediction delays and accurately captures the trends in wave height associated with storm surges. Among the physical factors studied, wind speed and atmospheric pressure emerge as the most important variables influencing storm surges. Additionally, the significance of wind direction is highlighted, as it plays a crucial role in determining whether the sea level rises or falls during the initial stages of a storm surge. It is expected that the proposed model has the potential to enhance response and preparedness measures in hurricane-prone areas, finding applications in coastal planning, infrastructure design, disaster management, and other aspects of ocean engineering.

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

Algorithmic uncertainties in geolocating social media data for disaster management

ABSTRACT The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. However, less than 1% of users geotag their messages, leaving geolocating users through user profiles or message content addresses increasingly crucial. This paper evaluates and visualizes the margin of error incurred when using user profiles or message-mentioned addresses to geolocate social media data for disaster research. Using Twitter data during the 2017 Hurricane Harvey as an example, this research assessed the inconsistencies in predicting users’ locations in various administrative units during each disaster phase using three geolocating strategies. The results reveal that the similarities between geotags, and user profile locations decrease from 94.07% to 64.56%, 43.9%, 31.82%, 27.05%, and 26.7% as the geographical scale changes from country to state, county, block group, 1-kilometer, and 30-meter levels. These similarities are overall higher than the agreements between locations derived from geotags and tweet content. The geolocation consistencies among the three methods remain stable across disaster phases. The impacts of uncertainties in geolocating Twitter data for disaster management applications were further unraveled. The findings offer valuable insights into the trade-off between spatial scale and geolocation accuracy and inform the selection of appropriate scales when applying different geolocating strategies in future social media-based investigations.

An integration of operations research and design science research methodology: With an application in hospital disaster management

This paper presents a conceptual hybrid model that embodies a synergistic approach by integrating principles from operations research (OR) with steps from the design science research methodology (DSRM). The model is applied to develop an evacuation support system for a group of hospitals located in Western Sydney, Australia, exposed to the risk of floods induced by climate change. By combining these two approaches, the conceptual hybrid model effectively overcomes the limitations associated with traditional DSRM and provides a comprehensive research framework for addressing OR problems. The research findings hold significant implications for academia, professionals, and policy makers engaged in the field of disaster risk management. Moreover, this study offers valuable insights into enhancing the effectiveness and efficiency of OR-based solutions in practical scenarios, thus contributing to the advancement of knowledge in this area.

Enhancing Research Outcomes through Drone-Generated Imagery and Photogrammetry Software Analysis

Drones have gained popularity in numerous fields, including education, where they are employed as valuable tools for enhancing students' knowledge. In particular, the use of drones in land surveying and mapping has revolutionized the industry, replacing manual and time-consuming methods from the 19th century. Unmanned aerial vehicles (UAVs) enable surveyors to cover vast areas easily, even in challenging terrains, while producing high-resolution maps and collecting extensive data efficiently. With the aid of automated software, quality control can be achieved with minimal training. The benefits of drone surveying encompass terrain modeling, surveying, and 3D mapping for GIS purposes, such as topography and volume measurements. The return on investment (ROI) from utilizing drones in mapping and surveying is substantial. Furthermore, UAVs find practical application in close-range mapping, particularly in engineering survey works that traditionally incur significant costs, labor, and time. Low-cost UAVs offer reliable information for various applications, including road design, bridges, and land surveys, matching the accuracy of conventional engineering surveys and policies, particularly for small-scale mapping. UAVs present a stable and rapidly advancing technology, positioning them as competitive alternatives to other surveying methods. This study consists of five phases: preliminary study, data collection, data processing, post-processing, and analysis, with a focus on UAVs as a data processing tool. The project demonstrates the utilization of UAV images and photogrammetry software, which involve capturing overlapping images of the Earth's surface using cameras mounted on drones or airplanes. These images are then processed with photogrammetry software to generate geo-referenced maps, ortho mosaic images, and other valuable outputs. The software is capable of processing both images and lidar points, offering versatility in its applications, including artistic modeling and surveying. The increasing popularity of UAV images and photogrammetry software extends to various fields, such as agriculture, military operations, disaster management, and artistic modeling. As UAVs become more affordable, their accessibility expands to a broader range of users. Aerial mapping with UAVs necessitates mission planning and ground control points, making drones valuable in mapping, search and rescue missions, transportation, and other disaster management applications. Multiple photogrammetry software options are available, each with distinct features and advantages. By leveraging UAV images and photogrammetry software, previously challenging images can be acquired, opening new possibilities for diverse applications.

Systematic Configuration for Hyperparameters Optimization in Transferring of CNN Model to Disaster Events Classification from UAV Images

Deep learning and computer vision-based approaches incorporated with the evolution of the relevant technologies of Unmanned Aerial Vehicles (UAVs) and drones have significantly motivated the advancements of disaster management applications. This research studied a classification method for disaster event identification from UAV images that is suitable for disaster monitoring. A Convolution Neural Network (CNN) of GoogleNet models that were pretrained from ImageNet and Place365 datasets was explored to find the appropriate one for fine-tuning to classify the disaster events. In order to get the optimal performance, a systematic configuration for searching the hyperparameters in fine-tuning the CNN model was proposed. The top three hyperparameters that affect the performance, which are the initial learning rate, the number of epochs, and the minibatch size, were systematically set and tuned for each configuration. The proposed approach consists of five stages, during which three types of trials were used to monitor different sets of the hyperparameters. The experimental result revealed that by applying the proposed approach the model performance can increase up to 5%. The optimal performance achieved was 98.77 percent accuracy. For UAV/drone applications, where a small onboard model is preferred, GoogleNet that is quite small in model size and has a good structure for further fine tuning is suitable to deploy.

Social resilience and disaster resilience: A strategy in disaster management efforts based on big data analysis in Indonesian's twitter users

Disasters have various causes, disaster management efforts, and actors involved. A systematic big data analysis is needed to identify social resilience to determine the quality of the country's resilience on disasters. This study aims to (1) determine perceptions about the causes of disasters and (2) understand perceptions of disaster management efforts. (3) identify actors involved in disasters. (4) analyze the relationship between social resilience and disaster resilience using large data sources. (5) formulate a disaster management. The research was conducted by describing in detail from the opinions of the twitter user community about disasters using the text mining method. The data retrieval and analysis process was carried out using Computer-Assisted Qualitative Data Analysis Software (CAQDAS) with MAXQDA series 2020, Gephi version 0.10.0 and SWOT analysis. The results of the study show: (1) Most of the perceptions of the causes of disasters are associated with religion; (2) Most of the perceptions about disaster management efforts are based on the application of disaster management at the recovery stage; and (3) The actors who are most involved in disaster management efforts are the security forces countries. (4) There is a strong relationship between social resilience and disaster resilience, as shown by each actor having a role in disaster management efforts. (5) There are nine formulations of development strategies in disaster management efforts. The limitation of this research is that it only uses big data from Twitter and social media sources. The implications of this research can be used as a reference for governments, organizations, communities, or others involved in disaster management efforts, especially in countries that have diversity and are prone to disasters.

A Scenario-Based and Game-Based Geographical Information System (GIS) Approach for Earthquake Disaster Simulation and Crisis Mitigation

The current research study aims to introduce the experience of implementing a serious game using the concept of game-based GIS approach for crisis management during earthquake disasters. In this study, we aimed to develop a game-based GIS approach and examine its efficiency for simulating earthquake rescue management in Tabriz city. In designing this game, typical scenario-based, game-based GIS methods and techniques were employed, and the proposed approach was applied to crisis management. To achieve this goal, we addressed the technical details regarding the development and implementation of the scenario-based and game-based GIS approach. Based on the results, game-based simulations can be considered an efficient approach for disaster simulation and can improve the skills of rescue teams. The outcome of this application is an intellectual game that almost all users at any age can play, and the game can challenge their ability to solve critical issues. The results are critical for explaining the effectiveness of rescue teams and crisis management facilities. As we intended to develop an approach for the simulation of earthquake disasters and emergency responses, we therefore conclude that the results of this study can also be employed to improve the skills of rescue teams and citizens for dealing with crises resulting from earthquake disasters. As a result of this research, the developed tool is published, together with this paper, as an open source and can be employed for any scenario-based analysis in other case studies. By presenting a-state-of-the-art approach, the results of this research study can provide significant contribution to further the development of GIScience and its applications for disaster and risk mitigation and management.

Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery

Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size of remotely sensed images of natural scenes on Earth. For such challenging problems, deep-learning-based algorithms have demonstrated amazing performances in recent years. Among these methodologies, transfer learning is a useful technique which employs the learned features already extracted from the pre-trained models from large-scale datasets for the problem at hand, resulting in quicker and more accurate models. In this study, we deployed cross-domain transfer learning for the land-cover classification of remotely sensed images of natural scenes. We conducted extensive experiments to measure the performance of the proposed method and explored the factors that affect the performance of the models. Our findings suggest that fine-tuning the ResNet-50 model outperforms various other models in terms of the classification accuracy. The experimental results showed that the deployed cross-domain transfer-learning system achieved outstanding (99.5% and 99.1%) accurate performances compared to previous benchmarks on the NaSC-TG2 dataset with the final tuning of the whole structure and only the last three layers, respectively.

Background-Mixed Augmentation for Weakly Supervised Change Detection

Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

Bipolar intuitionistic fuzzy graph based decision-making model to identify flood vulnerable region.

Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam ([Formula: see text]) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.

Ghana’s Disaster Management Role and Application of Information Technology

Ghana is susceptible to disasters including floods, earthquakes, domestic and industrial fires, rain and windstorms, epidemics, pest and insect infestation, boat and road accidents, and many others. There are no clear policies in place to help citizens cope with disasters or even report them if they occur. The research examines historical and current instrumental records, as well as the role of information technology in disaster management in Ghana. Furthermore, the study aims to assist state institutions in understanding the different types of ICT tools on which they can rely at each stage of a disaster to help inform, educate, and mitigate during a disaster. The survey was used to collect primary data from the study population, and the study’s methodology was also qualitative. Floods and wildfires, both domestic and industrial, are common in Ghana, according to the study's findings. It was also discovered that radio is the most widely used ICT tool for disaster reporting.

Disaster and risk management in outdoor recreation and tourism in the context of climate change

PurposeThe increasing frequency and intensity of the extreme weather events could cause devastating consequences in tourism. Climate change–related extreme weather events and their relation to tourism is an emerging field for education and research. The purpose of this study is to categorize the impact of climate change on tourist destinations with regard to extreme weather-related risks in outdoor recreation and tourism. Managerial implications for policymakers and stakeholders are discussed.Design/methodology/approachTo outline the risks from climate change associated with tourism, this study uses the Prisma analysis for identification, screening, checking for eligibility and finding relevant literature for further categorization.FindingsBased on a thoroughly examination of relevant literature, risks and threats posed by climate change could be categorized into following four areas: reduced experiential value in outdoor winter recreation; reduced value in beach scenery and comfort; land degradation and reduced biodiversity; and reduced value in personal safety and comfort in tourism. It also focuses on the significance of using big data applications in catastrophic disaster management and risk reduction. Recommendations with technology and data analytics to continuously improve the disaster management process in tourism education are provided based on findings of this study.Originality/valuePrimary contributions of this study include the following: providing a summarized overview of the risks associated with climate change in terms of tourist experiential value for educational implications; and revealing the role of data analytics in disaster management in the context of tourism and climate change for tourism education.

From science to practical applications in disaster management: views from UN-SPIDER

From science to practical applications in disaster management: views from UN-SPIDER