CHALLENGES OF ARTIFICIAL INTELLIGENCE APPLICATION FOR DISASTER RISK MANAGEMENT

Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

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.

Artificial Intelligence (AI) is transforming emergency response by optimizing ambulance routes, predicting emergency room demands, and assisting in natural disaster management. By leveraging machine learning, data science, generative AI, neural networks, and bio-inspired algorithms, AI enhances efficiency and effectiveness in critical situations. This chapter explores the integration of AI in emergency response, highlighting its potential to improve health and well-being outcomes during emergencies.

Background Of Study: Disaster can affect anyone, but children are the most vulnerable. School play an essential rile during disasters because children spend most of their time in such institutions, and teachers are among the untapped groups whose potential roles in disaster risk reduction and management are not yet fully realizes.1Disaster is defined as phenomena caused by environmental events or armed conflicts that lead to fatalities, injuries, stress, physical damage and economic breakdown of great significance. They occur on a scale that overwhelms local resources, usually requiring external assistance. Improving the effectiveness and efficiency of interventions, and the fairness of their distribution, is crucial in the field of disaster response. For that reason, increasing and improving the scientific evidence for disaster relief is essential. Research is also vital to accurately describe phenomena in disaster, also called humanitarian emergencies or crise. Problem Statement: ‘‘A study to assess effectiveness of self - instructional module (SIM) on knowledge regarding disaster management among school teachers in selected schools of Maharashtra. Objectives of the Study: 1. To assess the existing level of knowledge regarding disaster management among school teachers in selected schools of Maharashtra.2. To determine the effectiveness of self - instructional module on the level of knowledge regarding disaster management among school teachers in selected schools of Maharashtra.3. To find out association between pre - test levels of knowledge on disaster management with selected socio demographic variables. Methodology: Pre experimental research design, one group pre - test post - test research design was used to assess the effectiveness of Self - Instructional Module regarding Disaster Management among school teachers in selected school of Maharashtra. In this study 60 samples selected Non - Probability Purposive Sampling technique. The data obtained were analysed using descriptive and inferential statistics based on objectives and research questions of the study. Analysis and Result: In the school teachers with respect to the age majority 29 (48.9%) of the teachers belongs to 41 - 50 years, 25 (41.7%) of teachers belongs to 31 - 40 years, 5 (8.3%) of teachers from 51 and above year and only 1 (1.7%) was in from 21 - 30 year of age. In case of gender majority 35 (58.3%) were male and 25 (41.7%) were female. Regarding education status of the school teachers the majority 43 (71.7%) were educated up to B. Ed., 13 (21.7%) were M. Ed. and 4 (6.7%) were educated up to D. Ed. The majority 23 (38.3%) teachers having 11 - 15 years’ experience in teaching, 18 (30.0%) are having 6 - 10 years’ experience and 19 (31.7%) are having 16 years and above experience in teaching. Majority 38 (63.3%) were used T. V. as source of knowledge regarding disaster management. and 22 (36.7%) were used newspaper as source of knowledge regarding disaster management. Knowledge wise comparison of study subject in pre - test revels 30 (50.0%) were had poor knowledge regarding disaster management, 24 (40.0%) were had average knowledge regarding disaster management and 6 (10.0%) teachers were had good knowledge regarding disaster management. In the post - test result 0 (0.0%) were had poor knowledge regarding disaster management, 0 (0.0%) were had average knowledge regarding disaster management and 60 (100.0%) teachers were had good knowledge regarding disaster management. There is no significance association between knowledge score and demographic variables. Conclusion: After the present study to assess effectiveness of Self Instructional Module regarding disaster management among school teachers, study proves that having the need to improve the knowledge regarding disaster management. And also the Self Instructional Module is effective for to improve knowledge.

The radar warning and information system for applications in disaster management (RAWIS) is being designed and constructed in a consortium comprising eight partners. It is intended for civil protection applications and will support emergency teams engaged in complex mission contexts. This paper gives an overview of the overall project and addresses some aspects of the sub-projects.

UAV usage and interest have shown great popularity recently, especially for commercial multirotor. It is cheap, easy to use, flexible, and has many capabilities. That is why multirotor is widely chosen in disaster management. As a technology, it possesses certain aspects. Review of how the implementation of multirotor UAV in disaster management is important to gain improvement in the technology and evaluate at how we use it. This paper provides insight into commercial multirotor UAV campaign in disaster management through literature study and personal experience in a related theme. Similar ideas on the development of UAV and its application in disaster management then combine with personal experience was used to identify the advantage, disadvantages, challenges, and opportunities of multirotor drone applications. The result suggested that multirotor UAV has limited capability in conducting disaster management campaigns. The weakness in deploying multirotor is related to the lack of technology which may be overcome in the future as the development in hardware and software technology.

Natural disasters, including earthquakes, wildfires, floods, and disease outbreaks, have catastrophic consequences for human lives, infrastructure, and economies worldwide. Their increasing frequency and intensity, exacerbated by climate change and urbanization, present significant challenges to disaster management. Traditional approaches often struggle to cope with the scale and complexity of modern disasters. This narrative review examines the potential of artificial intelligence (AI) to transform disaster management, exploring its applications across the disaster cycle. By assessing AI's role in damage assessment, early warning systems, resource allocation, and post-disaster rehabilitation, the review identifies opportunities to enhance disaster response and recovery efforts. Additionally, it addresses challenges related to AI implementation, such as algorithmic bias, data quality, and ethical considerations, to develop equitable and robust AI-driven disaster management strategies. A comprehensive literature review was conducted to identify key research on AI’s role in early warning systems, disaster management, damage control, resource allocation, and post-disaster recovery. AI holds significant potential to improve disaster response and recovery. It can enhance early warning systems, optimize resource allocation, and improve damage assessment. Furthermore, AI can support post-disaster repair and rehabilitation efforts. However, challenges such as data quality, ethical concerns, and algorithmic bias must be addressed to ensure the responsible and effective application of AI in disaster management. By harnessing the power of AI, we can build more resilient societies and reduce the impact of future disasters. To fully realize AI’s potential, it is essential to integrate it into research, development, and ethical frameworks, ensuring its responsible and efficient implementation.

Advancement in the satellite imaging techniques has produced wider applications in disaster reduction and management. Considering the crucial role of rapid detection of landslides in disaster management, this research paper exploits the capability of sentinel-1A imagery in landslide detection. In image processing and feature detection techniques, machine learning based classification methods have gained popularity by proving its potential in mapping of susceptible landslides. Therefore, the primary objective of this research paper is automatic identification of landslides by using machine learning based classifier Random Forest (RF). To accomplish the objective, two Sentinel-1A GRD images, pre and post-event, covering Srinagar-Rudraprayag region, India has been processed with DEM derived products slope, aspect and elevation for landslide detection along the Alaknanda River and National Highway-58. For validation of the results, Google Earth Historic Imagery has been utilized. From the observation of results, RF method confirms its potential by achieving 95% in accurate detection of landslides.

Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed.

The World Meteorological Organisation defines climate as long-term weather patterns observed over decades or centuries, and these patterns have changed dramatically in recent years, mainly because of human activities like burning fossil fuels and cutting down forests. Artificial Intelligence (AI) has emerged as a transformative technology capable of addressing climate-related challenges across various sectors. This review explored the intersection of AI and climate change mitigation, highlighting how AI technologies such as machine learning, deep learning, natural language processing, and remote sensing contribute to monitoring emissions, forecasting weather, optimising energy systems, and supporting sustainable agriculture. The paper also discussed AI’s role in disaster risk management by enabling rapid data analysis and decision-making during emergencies. The findings revealed that AI's value in addressing climate change comes from its capacity to monitor environmental factors, forecast future conditions, and optimise resource use. Despite its immense potential, the deployment of AI in climate action faces several challenges, including high energy consumption, data scarcity, ethical concerns, and infrastructure limitations, particularly in under-resourced regions. Nevertheless, advancements in energy-efficient AI models, integration with IoT and satellite systems, and developments in decentralised learning techniques present significant opportunities for the future. This paper concluded that while AI is not a standalone solution, it is a critical component in the global climate mitigation strategy. With the right investments in education and research, AI has the potential to become a cornerstone of global climate action. Again, responsible implementation and cross-sector collaboration, AI can accelerate progress toward a more sustainable and climate-resilient future.

The role of GIS-enabled mobile applications in disaster management: A case analysis of cyclone Gaja in India

This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.

The interaction between humans and the environment is crucial for the survival of ecosystems and human societies. Understanding the impact of human activities, particularly carbon dioxide (CO2) emissions, on climate change is essential for developing effective mitigation strategies. This study investigates the role of artificial intelligence (AI) in addressing climate change by enhancing disaster prediction, optimizing energy consumption, and reducing emissions. AI applications in weather forecasting and disaster management provide accurate, real-time data, enabling proactive responses to extreme weather events and natural disasters. Additionally, AI-driven energy optimization can support the transition to renewable energy sources and improve efficiency in industrial and urban systems. Complementary strategies, such as reforestation and sustainable resource management, further contribute to emission reduction. The findings emphasize that technological solutions alone are insufficient; coordinated global efforts and policy implementation are critical to achieve sustainable outcomes. By integrating AI with ecological and societal measures, it is possible to mitigate the adverse effects of climate change and enhance resilience to environmental challenges. This research underscores the transformative potential of AI in climate action, highlighting that innovative technological interventions, when combined with sustainable practices, can significantly improve global climate mitigation efforts. The study provides evidence that leveraging AI in conjunction with human-driven environmental strategies offers a practical pathway toward a sustainable and resilient future.

Artificial Intelligence (AI) is transforming almost all spheres of life ranging from employment, medical practices, defence operations, urban infrastructure, banking and disaster management. Deploying AI-based systems during disasters to assist in forewarning and relief efforts can yield positive and negative results. While strong data can create robust and swift targeted delivery of relief measures, bias and discriminatory consequences resulting in a compromised AI can disrupt and displace all efforts. To investigate these angles, the authors explore the ethical and legal concerns raised by developing governance regimes for AI systems. The core of the problem is to develop an AI governance based on the principles of accountability, fairness, and transparency. The chapter argues that ethics and agency constitute a crucial element in appreciating AI systems in an algorithmic society. The chapter will look at the opportunities and ambiguities of borrowing the principles from disaster management and environmental law into the application of AI. Drawing upon the literature, the authors locate legal personality, liability, regulation and possible considerations of making AI a viable tool in disaster management.