Evaluating the impact of crop waterlogging and flood disasters using multi-source data: a case study of the Sanjiang Plain

Flood disaster is a dangerous disaster, an event that occurs due to overflow of water resulting in submerged land is called a flood disaster. Almost every year Bantul Regency is affected by floods due to high rainfall. The flood disaster that struck in Bantul Regency made the Bantul District Disaster Management Agency (BPBD) difficult to handle so that it needed a mapping of the level of the impact of the flood disaster to minimize the occurrence of floods and provide information to the public.This study will create a system to map the level of impact of floods in Bantul Regency with a decision support method namely Multi Attribute Utility Theory (MAUT). The MAUT method stage in determining the level of impact of flood disasters through the process of normalization and matrix multiplication. The method helps in determining the areas affected by floods, by managing the Indonesian Disaster Information Data (DIBI). The data managed is data on criteria for the death toll, lost victims, damage to houses, damage to public facilities, and damage to roads. Each criteria data has a value that can be used to determine the level of impact of a flood disaster. The stages for determining the level of impact of a disaster require a weighting calculation process. The results of the weighting process display the scoring value which has a value of 1 = low, 2 = moderate, 3 = high. To assist in determining the affected areas using the matrix normalization and multiplication process the process is the application of the Multi Attribute Utility Theory (MAUT) method.This study resulted in a mapping of the level of impact displayed on google maps. The map view shows the affected area points and the level of impact of the flood disaster in Bantul Regency. The mapping produced from the DIBI data in 2017 produced the highest affected area in the Imogiri sub-district. The results of testing the data can be concluded that the results of this study have an accuracy rate of 95% when compared with the results of the mapping previously carried out by BPBD Bantul Regency. The difference in the level of accuracy is because the criteria data used are not the same as the criteria data used by BPBD in Bantul Regency so that the accuracy rate is 95%.

Purpose: Disasters may have direct and indirect impacts on the population’s health and healthcare system. Deaths, injuries, psychological effects, and diverse diseases can be measured in varying degrees of rigor and substance. Indirect impact factors (e.g., losses to primary healthcare and living conditions, damages to healthcare systems and external infrastructures, provision of water and/or electricity) including their consequences are very often not subject of attention. The purpose of this study is to identify various impacts of different European disasters on health system performance, security and health protection with focus on psycho-social support. This study is part of the international multidisciplinary project PsyCris (PSYcho-social Support in CRISis Management) that is funded by the European Union with the overall objective to improve psycho-social support in crisis management. Methodology: Based on different impact models the authors present an analysis of impacts of five European disasters. The collection of the data was organized by a questionnaire that serves as assessment tool for each disaster. The questionnaire consists of different questions concerning the disaster and its management. In this regard existing material (e.g., reports, articles, films, photos) served as foundation in answering the questions. Additionally, interviews with people, who were involved in the management of each disaster, complemented the questionnaire. Results: The analysis of the case studies has shown that each disaster causes aftermaths in different fields. Many identified impacts are the result of a learning process because of inadequate outputs during and after the disaster. For example, because of the fact that during the avalanche in Galtur (Austria) the psychosocial support did not work, the Red Cross founded the KIT (crisis intervention team), which represents a long-term impact on the health system performance. In Lithuania we have identified health protections measures such as individual consultations in advance of a flood with the objective to inform newly settled residents to be better prepared in the case of the flood (e.g., long-term impact concerning preparedness planning). Conclusions: Different impacts of disasters on the health system performance (e.g., changes and adaptions in medical, psychological or psychiatric treatment, psycho-social support) and on security and health protection (e.g., optimisations in contingency/preparedness planning, infrastructure, training, increase in security research funding activities, information and communication measures) have been identified. The analysed communities have engaged in different types of emergency management and risk reduction interventions to minimize further disaster’s impacts. The results have shown that hazard mitigation and emergency preparedness practices can reduce direct and indirect impacts because of reflections and learning experiences.

Multi-source data represents a complex data type. This article proposes a method for the comprehensive utilization of multi-source data suitable for distribution network line loss calculation. Firstly, to facilitate data fusion, multi-source measurement data is converted. Secondly, to ensure consistency in measurement time snapshots, a specific measurement time from a Distribution Phasor Measurement Unit (D-PMU) is selected as the benchmark. SCADA data undergoes time registration and data interpolation, while smart meter data is time-aligned using a combination of “measured values + predicted values.” Subsequently, the time-series data is filtered to obtain more accurate distribution network data, and then multi-source data fusion is achieved based on the Dempster-Shafer (D-S) evidence theory. By conducting power flow state analysis based on the high-quality fused data, distribution network data can be utilized in a more refined manner, addressing the shortcomings of traditional methods. Finally, a 10kV distribution network is selected as a case study to obtain the power flow state using the multi-source data fusion method, upon which the distribution network line loss is calculated. The results demonstrate that the proposed method can effectively accomplish the comprehensive utilization of multi-source data and improve the accuracy of distribution network data applications.

Floods are one of the natural disasters that often occur in Indonesia, including in Riau Province. Kampar Regency, especially the Perhentian Raja area, is one of the areas prone to flood disasters. caused by the morphological and physical conditions of the area which is dominated by lowlands and surrounded by large rivers such as the Kampar and Siak rivers and other rivers in Kampar Regency. This research aims to 1). Knowing the impact of the flood disaster at SMA Negeri 1 Perhentian Raja, Kampar Regency, Riau Province. 2). Analyzing the level of preparedness of students facing flood disasters at SMA Negeri 1 Perhetian Raja, Kampar Regency, Riau Province, 3). Formulating a strategy for developing students' preparedness for flood disasters at SMA Negeri 1 Perhentian Raja. This research was conducted using mixed research (Mix Methods) with quantitative and qualitative approaches, and using the AHP method (analytical Hierarchy Proces) to formulate strategies for developing student preparedness. Data collection using interviews, questionnaires, FGD (Focus group discussion), and analysis) . The research results show that 1). The impact of the flood disaster at SMA Negeri 1stop Raja showed a light impact on the physical and environmental aspects of the school. 2). The level of preparedness of students in facing flood disasters at SMA Negeri 1 Perhentian Raja was obtained using 5 parameters. The total number of parameters entered the average index of 86.4% in the very prepared category. 3). The strategy for developing students' preparedness to face flood disasters at SMA Negeri 1 Perhentian Raja uses the method Analytical hierarchy proces got 9 alternatives. This alternative was formulated to reduce the risk of flood disasters at SMA Negeri 1 Perhentian Raja.

– One of the most important issues in flood risk management is finding a way to cope with uncertainties. Despite centuries of experience with flood management, flood disasters become more frequent and are increasing in severity due to climate change. This work examined flood disaster adaptation strategies among the people of Guma Local Government Area, Benue State. Data on the demographic characteristics of respondents, flood frequency, duration, and impact of flood disasters on people of the study area, as well as flood mitigation and adaptation strategies, were obtained using a questionnaire. A total of 380 respondents were sampled using random sampling. The data were analyzed using frequencies and percentages and presented in tables. Results revealed that the respondents are susceptible to the impact of flood disasters due to their socio-demographic characteristics. Furthermore, the results show that the frequency of flood disasters is biennial while its duration is between one to two weeks. Flood also impacts both directly and indirectly, involving mostly destruction of farm crops, and disruption of transportation. Lastly, the results show that the respondents mostly clear-filled/blocked drainages around them to mitigate the impact of flood disasters and many of the respondents construct wooden bridges across drainages/gutters in order to adapt to flooding in their areas. The study recommends that non-flood-sensitive economic activities should be embraced to mitigate the impact of flooding in the study area. There should also be public enlightenment and sensitization on the need to adopt both structural and nonstructural measures of adapting to flood disasters as climate change continues to trigger more severe, and frequent floods.

Analysis of public transit operation efficiency based on multi-source data: A case study in Brisbane, Australia

Climate disasters have become increasingly frequent in India, severely affecting the railway infrastructure every year. Physical damages to railway tracks, bridges, and signaling systems, caused by floods, cyclones, and landslides, are well documented. However, the impact of these disasters on the railway infrastructure was beyond direct physical damages. This paper aimed to explore the impact of climate disasters on railway infrastructure in Northeast India using case study approach. Three cases were studied to analyse the impact of climate disasters on railway infrastructure, including geological disasters and extreme weather. Infrastructure development and operation of railway transport system in Assam, Mizoram, and Manipur proved to be challenging, especially when coping with natural disasters, such as floods, landslides, and earthquakes. This paper found that disruption of railway services was associated with geo-physical structure of the region, which triggered the disaster vulnerability. The results showed that climate disasters had a significant impact on railway infrastructure in Northeast India in many aspects. Formulation and implementation of strategic policies might reduce the disaster risks. Therefore, policymakers and Ministry of Railways, Government of India should consider this possible probability approach over environmental determinism.

Worldwide there has been a disease transition to noncommunicable diseases due to population aging, increasing obesity and decreasing physical activity. This combined with an increasing frequency of natural disasters has created challenges for disaster and health systems. Natural disasters can and do damage public health infrastructure such as services and supplies, resulting in acute exacerbation of noncommunicable diseases. To reduce the risk, this research explored the impact of cyclone, flood and storm related disasters on public health infrastructure and the management of noncommunicable diseases in Queensland, Australia, and used the findings to develop mitigation strategies. Data was collected, analysed and integrated over three sequential phases: literature reviews; focus groups and interviews; and a modified Delphi process. People with noncommunicable diseases found to be at greatest risk were those: with cardiovascular, respiratory and renal diseases; undergoing cancer treatment; and with unstable diabetes; and mental health conditions. There were 31 mitigation strategies identified across 12 public health infrastructure themes. Specific strategies include: designated primary healthcare hubs post disaster; streamlining processes for patients to access medication after a disaster; the need for water treatment plants to have back-up power; good hygiene practices implemented at evacuation centres; and consistent and clear messages about where people can go for assistance after a disaster. These findings informed development of a conceptual framework and options for implementing mitigation strategies to sustainably minimise the impact of disasters on people with noncommunicable diseases.

A disaster is an occurrence that cannot be predicted when it will occur and can cause injuries or lives, as well as result in damage and loss. Preparedness efforts in facing disasters must be increased to reduce the risk and impact of disasters. One of the factors in disaster preparedness is the level of knowledge. Knowledge is one of the main factors and is the key to preparedness for facing disasters. The knowledge a person has can usually influence attitudes and concern for preparedness in anticipating a disaster. Method: quantitative with descriptive research methods, the total sample was 53 people, in May 2024 in RT.02 RW.01 Joyontakan Village, Surakarta City. The sampling technique used total sampling. Results: An overview of the level of community knowledge in RT.02 RW 01, Joyontakan Subdistrict, Surakarta City showed that the majority of the good knowledge level was 57%. Discussion: The community will have good knowledge that can be obtained from the experience of experiencing flood disasters in their area. This experience can provide additional knowledge for residents of areas that are prone to flood disasters. The flood disaster that has been experienced by residents has influenced the community's attitude and concern to be ready and ready to anticipate when a flood disaster occurs.

The earthquake that struck the Japanese port city of Kobe on January 17, 1995, was the most severe quake ever to strike a modern urban area. It has become the most studied, analyzed, and discussed natural disaster in history. What I propose to add to this dialogue is an economist's overview of what he saw in Kobe 19 months after the event and what he learned during the ensuing 6 months.

Natural or man-made disaster? Lessons from the extreme rain and flood disaster in Zhengzhou, China on "2021.7.20"

As a pillar industry of China’s economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate; (2) the average housing vacancy rate (HVR) of houses in Longquanyi District’s residential community is 46%; (3) the HVR rises progressively with the increase in the distance from the city center; (4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious; (5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development.

With the use of the concept Central Activities Zone in the Shanghai City Master Plan (2017-2035) to replace the traditional concept of Central Business District, core areas such as Shanghai Lujiazui will be given more connotations in the future construction and development. In the context of todays continuous urbanization and high-speed capital flow, how to identify the development status and vitality characteristics is a prerequisite for creating a high-quality Central Activities Zone. Taking Shanghai Lujiazui sub-district etc. as an example, the vitality value of weekday and weekend as well as 19 indexes including density of functional facilities and building morphology is quantified by obtaining multi-source big data. Meanwhile, the correlation between various indexes and the vitality characteristics of the Central Activities Zone are tried to summarize in this paper. Finally, a neural network regression model is built to bridge the design scheme and vitality values to realize the prediction of the vitality of the Central Activities Zone. The data analysis method proposed in this paper is versatile and efficient, and can be well integrated into the urban big data platform and the City Information Modeling, and provides reliable reference suggestions for the real-time evaluation of future urban construction.

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

The inland rivers under the geographical pattern of the mountain-basin system in the arid areas of China have special ecosystem types and landscape appearances. The inland river basin, represented by the Tarim River, has given birth to different ecosystem types and landscape appearances of river corridors-vegetation or farmland patches-desert matrix patterns. Through multi-source remote sensing(RS) data, geographic element monitoring data, ecological environment statistical survey data, model simulation and other multi-source data, the data verification and normalization are carried out, and the ecological environment quality(EEQ) characteristics are obtained through model operation and analysis.The results show that due to the constraints and influences of the mountain-basin system, precipitation shortage, strong evaporation, and severe drought lead to the widespread salinization of the basin. In addition, the sparse vegetation and the frequent occurrence of sandstorm disasters have led to drastic changes in the spatiotemporal dynamics of desertification. Under the multiple stresses of climate change and the development of man-made water resources and land resources, a series of changes have taken place in the EEQ. The analysis of panchromatic aerial RS images (1959), color aerial RS images (1992), JERS-1 OPS RS images (1995) and MODIS RS images (2023) shows that desert riparian forests show a discontinuous distribution on the north and south sides of the main stream corridor, and the vegetation tends to be degraded from the source area to the upper, middle and lower reaches of the main stream. Since 2000, the water resources allocation project in the basin has alleviated the vulnerability of water resources to spatiotemporal changes, slowed down the degradation trend of EEQ in the basin, and significantly improved the EEQ in some areas.Based on the systematic analysis of multi-source data such as hydrology, soil, climate, vegetation and landscape pattern changes in the basin, combined with the SSP climate scenario model, it is found that the future temperature and precipitation will show an upward trend under the SSP2-4.5 and SSP5-8.5 scenarios. Through the establishment of the ecological risk index (ERI) model, the quantitative evaluation showed that the ERI values of the Aksu River Basin in the headwaters were 0.08 and 0.06 in 1998 and 2023, respectively, indicating that the EEQ was in a stable and improving state in the past 25 years, and the EEQ continued to improve. It is estimated that by 2040, drought and flood disasters in the basin will be further aggravated, and the evolution of EEQ will be complex and uncertain.