Research on Safety and Integrated Disaster Prevention System Based on Big Data Technology

Safety integrated disaster prevention system, as a guarantee of national safety, especially to reduce the serious consequences of disasters, promote the steady development of the economic and social level, has important practical value for the comprehensive study of safety and disaster prevention system. However, the current application and update of such systems by relevant government agencies and the social level cannot effectively follow the development needs of the society and the industry, and there is an urgent need for effective reform. Based on this, this paper first analyzes the problems existing in the research and construction system of big data technology in the security and integrated disaster prevention system, and then gives the construction strategy of the research system of safety and disaster prevention in view of these problems

Role of Machine Learning, Deep Learning and WSN in Disaster Management: A Review and Proposed Architecture

Disasters are occurrences that have the potential to adversely affect a community via casualties, ecological damage, or monetary losses. Due to its distinctive geoclimatic characteristics, India has always been susceptible to natural calamities. Disaster Management is the management of disaster prevention, readiness, response, and recovery tasks in a systematic manner. This paper reviews various types of disasters and their management approaches implemented by researchers using Wireless Sensor Networks (WSNs) and machine learning techniques. It also compares and contrasts various prediction algorithms and uses the optimal algorithm on multiple flood prediction datasets. After understanding the drawbacks of existing datasets, authors have developed a new dataset for Mumbai, Maharashtra consisting of various attributes for flood prediction. The performance of the optimal algorithm on the dataset is seen by the training, validation and testing accuracy of 100%, 98.57% and 77.59% respectively

Prediction of Land Use Changes and Flood Risk Maps Using GIS and ANN Modelling in Perak

Flood is one of the disastrous events that lead to economic and properties loss which frequently impact Perak, Malaysia. The landscapes of Perak have significantly changed throughout the year due to the population expansion and development. Considering the effect of land use changes is important to develop flood risks map for planning mitigation approaches

Improving Flood Management Planning Decisions through Multi-Criteria Decision Analysis: A Case Study of Health and Safety Facility Management in Kelantan, Malaysia

This thesis investigates interactions in optimising Multicriteria Decision Analysis (MCDA) applications in flood decisions through interconnected studies, enhancing understanding of complex relationships and their implications. Based on Systematic Literature Review (SLR) of MCDA application trend in water-related disaster management, Analytical Hierarchical Process (AHP) is the common technique employed. According to Disaster Management Phase, mitigation is the primary focus, highlighting gaps in other phases. Future exploration of its potential in other phases and feasibility of alternative techniques is suggested for beneficial practical application. Chapter 3 focuses on criteria selection for FMP through experts’ interview and SLR. The Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) analysis framework clusters the criteria, identifying 40 final criteria as potential and trade-off factors. Integrated domains are less represented, suggesting a framework coupling MCDA and PESTEL for criteria selection in the future. Building on 40 criteria identified, Chapter 4 analyses the criteria using AHP and Quadrant Matrix Analysis with experts. Results revealed the importance of complementing criteria importance and certainty for better decisions. The Weather Reflection model introduced enhances the proposed framework for criteria analysis, significantly benefiting decision-makers. Chapter 5 explores integrating spatial and MCDA techniques as a Decision Support System (DSS). A conceptual framework with five phases facilitates DSS development is proposed, which is adaptable to various domains. A DSS prototype developed in Chapter 6 based on previous chapter, which employed four MCDA techniques. Its functionality is assessed through case studies, determining feasible locations for future hospital buildings. The prototype’s acceptability is validated based on Content Validity Index (CVI) thresholds, thus aiding flood decision planning. Collectively, these studies advance practical MCDA applications, offering actionable insights for decision-makers navigating MCDA challenges. By proposing frameworks for criteria selection, analysis, and DSS, and presenting a validated prototype, this thesis contributes to interdisciplinary knowledge, optimising MCDA in flood planning

A Review on Applications of Big Data for Disaster Management

International audienceThe term " disaster management " comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review