Earthquake Disaster Management Research Articles

Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities

PurposeEarthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.Design/methodology/approachThis study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.FindingsThe study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.Originality/valueCurrent methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

A comparative study of earthquake disaster management laws between USA and Indonesia.

The findings of this study are expected to serve as evaluation material and to improve government effectiveness in dealing with natural disasters.

Policy Synergy Model in Earthquake Disaster Management in Cianjur Regency

This research is based on a phenomenon that indicates the ineffectiveness of policy synergy in dealing with the impact of the earthquake disaster in Cianjur Regency. This phenomenon can be observed from several indications, including the lack of fully effective communication patterns among stakeholders involved in disaster management. Apart from that, it was detected that there were still differences in perceptions from some stakeholders in dealing with technical matters, so that the provision of assistance tended to be uneven. There were delays in evacuating and relocating those affected by the disaster. Through desk study methods and approaches, research results were found, including; To synergize earthquake disaster management policies in Cianjur Regency, clear parameters are needed, so that all stakeholders can carry out their duties in accordance with their roles and functions. The parameters referred to include, among other things, the existence of similarities in perception, potential resources, communication patterns, attitudes of policy actors, and organizational structure. Similarity of perception among stakeholders in disaster management will be effective if a common perception is established regarding objectives, content, programs and implementation. To translate disaster management policies, potential resources are needed which include human resource capabilities, budget, facilities and technology. Meanwhile, to facilitate communication in disaster management, clarity, speed and accuracy of communication patterns are needed, then there needs to be socialization and cooperation as well as completeness of information. To build the integrity and trust of the public in the authorities in dealing with the impacts of disasters requires the attitude of policy actors who have responsiveness, consistency and commitment. Meanwhile, to help facilitate the process of dealing with the impact of disasters, an organizational structure is needed which includes; aspects of division of authority, support from all stakeholders and clarity of procedures (SOP).

A Novel Approach for Earthquake Prediction Using Random Forest and Neural Networks

INTRODUCTION: This research paper presents an innovative method that merges neural networks and random forest algorithms to enhance earthquake prediction.
 OBJECTIVES: The primary objective of the study is to improve the precision of earthquake prediction by developing a hybrid model that integrates seismic wave data and various extracted features as inputs.
 METHODS: By training a neural network to learn the intricate relationships between the input features and earthquake magnitudes and employing a random forest algorithm to enhance the model's generalization and robustness, the researchers aim to achieve more accurate predictions. To evaluate the effectiveness of the proposed approach, an extensive dataset of earthquake records from diverse regions worldwide was employed.
 RESULTS: The results revealed that the hybrid model surpassed individual models, demonstrating superior prediction accuracy. This advancement holds profound implications for earthquake monitoring and disaster management, as the prompt and accurate detection of earthquake magnitudes is vital for effective mitigation and response strategies.
 CONCLUSION: The significance of this detection technique extends beyond theoretical research, as it can directly benefit organizations like the National Disaster Response Force (NDRF) in their relief efforts. By accurately predicting earthquake magnitudes, the model can facilitate the efficient allocation of resources and the timely delivery of relief materials to areas affected by natural disasters. Ultimately, this research contributes to the growing field of earthquake prediction and reinforces the critical role of data-driven approaches in enhancing our understanding of seismic events, bolstering disaster preparedness, and safeguarding vulnerable communities.

Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)

Abstract. Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (the Database of Observed Damage – DaDO). Six models were considered: regression- and classification-based machine learning models, each using random forest, gradient boosting, and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light-based system used; for example, during the post-disaster period (green, yellow, and red), 68 % of buildings were correctly classified. The results obtained by the machine-learning-based heuristic model for damage assessment are of the same order of accuracy (error values were less than 17 %) as those obtained by the traditional RISK-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered.

The Effect of Earthquake Disaster Management Preparedness Knowledge and Training on Midwife Preparedness in Central Aceh Regency, Indonesia

The threat of an earthquake has received wide attention, because it is sudden, predictable, but difficult to determine when it will occur. Predictions are based on the monitoring of seismic activity, historical records and observations. Disasters always have an adverse impact, such as damage to physical facilities and infrastructure (residential housing, office buildings, schools, places of worship, roads, bridges and others). Midwives are health workers who generally work in Health Center or in the community/community closest to the impact of the disaster. The contribution of the midwife to disaster/emergency risk reduction or preparedness is very important. Meanwhile, midwives are often not included in the workforce for disaster preparedness at local, national and international levels. This study aimed to determine the effect of knowledge and training on earthquake disaster preparedness on the preparedness of midwives in Central Aceh Regency. This study uses a survey research design that is analytic in nature with an approach cross-sectional which is measured at the same time as the research takes place and tested through hypothesis testing. The sample is 208 samples. The sampling method in this study was carried out proportionally in proportion to the total population spread across 14 Districts in Central Aceh Regency. The results of the study show that there is an influence of knowledge variables (p = 0.018), and disaster training (p = 0.000). This shows that knowledge and training have a relationship and influence on earthquake disaster preparedness in Central Aceh Regency.

ICT-Based Lombok Disaster Management Communication Model in Indonesian

When a natural disaster occurs, information and communication technology (ICT)-based disaster communication must be carried out, especially from the government to the affected community. The needs for information on the location and the number of victims and data collection on their needs are highly required to ease officers and volunteers to assist disaster victims. When an earthquake occurred on July 29, 2018, the community was in a state of panic, anxiety, and fear, making it prone to the confusion of information, resulting in slow disaster management. This study aims to determine the communication model of Lombok earthquake disaster management based on ICT carried out by the Public Relations of the North Lombok Regency Government to the people of Gangga Village, North Lombok. This research utilized a descriptive qualitative method by conducting in-depth interviews with the Public Relations of the North Lombok Regency Government, the people of Gangga Village, North Lombok Regency, and the North Lombok Regional Disaster Management Agency (BPBD). The results disclosed that the ICT-based disaster communication model during the July 29, 2018 earthquake integrated related Regional Work Units (SKPDs), such as the Public Relations, BPBD, Public Works Service, and Health Service through Handy Talky (HT), Hand Phone (HP), websites, and social media. The North Lombok Regency Government also explicitly coordinated with the Ministry of Communication and Information, who asked the NTB Regional Frequency Spectrum Monitoring Center to continuously coordinate with telecommunications operators, ORARI, and RAPI in NTB to ensure excellent telecommunications services for the community

Harmonizing Seismic Vulnerability Data for Effective Seismic Risk Assessment: Expert Judgment Method and Findings from a Multi-Stakeholder Study in Indonesia

Seismic Risk Management (SRM) is crucial for mitigating the impact of earthquakes, encompassing earthquake preparedness, response planning, building design, and emergency management systems. The 2006 Yogyakarta earthquake in Indonesia resulted in significant loss of life, infrastructure damage, and economic impact, underscoring the importance of Seismic Risk Assessment (SRA) for effective risk reduction. Seismic vulnerability functions are commonly used for SRA, but data collection can be expensive and challenging, and results may be biased due to expert judgment. In Indonesia, variability in house damage state definitions among agencies poses challenges in harmonizing data. This study used expert judgment to harmonize differences in damage state variables resulting from variable references. Data on damage states from five (5) agencies were collected and harmonized through input from earthquake engineering experts via online questionnaires. The resulting data identified five (5) types of damage and reduced variables to nine through harmonization. Twenty (20) experts with diverse backgrounds, including academics, researchers, contractors, consultants, volunteers, and government stakeholders, participated in the study through online questionnaires, and the results were processed and analyzed. The expert judgment method successfully harmonized the differences in damage state variables, ensuring a consistent, reliable, and accurate assessment of building damage, particularly housing damage. The findings of this study underscore the crucial role of expert judgment in harmonizing data for seismic risk assessment. The survey gathered input from experts with diverse backgrounds, including government stakeholders and academics, enriched the study's findings and contributed to a comprehensive understanding of earthquake disaster management. Through expert judgment, the study successfully consolidated variables from different agencies into nine (9) harmonized variables for assessing building damage. Furthermore, experts proposed an additional fourteen (14) factors that need to be considered in residential house damage inspection forms, providing critical information for estimating the extent of damage and informing decisions about repairs or demolition. These variables can be categorized into Rapid Visual Screening (RVS) and observation in more detail, aiding in effective earthquake risk management. However, further validation through comparative literature and field analysis is needed to enhance accuracy and adaptability and explore the potential of incorporating soft computational algorithms in seismic vulnerability assessments as a promising approach.

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

Assessment of Vulnerability Caused by Earthquake Disasters Based on DEA: A Case Study of County-Level Units in Chinese Mainland

Earthquake activity can generate huge energy in a short period of time, bringing enormous risks to people’s lives and property safety. This poses a great challenge to regional sustainable development. Meanwhile, due to the complex mechanism, seismic activity is difficult to accurately predict. Therefore, it is of great significance to explore how to reduce earthquake disaster losses from the perspective of human society. In this study, we use vulnerability to reflect the relative impact of earthquake disasters on different counties. The vulnerability caused by earthquakes is calculated with the data envelopment analysis (DEA) method. We use CCR and BCC models to further decompose vulnerability into pure technology vulnerability and scale vulnerability. This study analyzes 69 earthquake disasters that occurred in the Chinese mainland from 2013 to 2020 and explores the influencing factors of pure technology vulnerability from both quantitative and qualitative perspectives. Three main conclusions are drawn. First, four factors, including the added value of the secondary industry, gross domestic product (GDP) per capita, investment density of fixed assets and energy released by earthquakes, have a significant impact on the pure technical vulnerability of counties caused by earthquake disasters. Second, in the samples under consideration, the average vulnerability of the regions with an earthquake magnitude below 5.0 is higher than that of the regions with an earthquake magnitude between 5.0 and 6.0. There are deficiencies in organization, management and facilities in regions with a small earthquake risk. Third, through qualitative analysis, it is shown that the seismic function of buildings affects the vulnerability of counties facing earthquake disasters. The results of the research can provide decision makers with new insights into earthquake prevention and disaster reduction management.

The Effectiveness of Earthquake Disaster Management Policy in Indonesia

Indonesia is highly susceptible to a variety of natural and man-made disasters, including earthquakes, volcanoes, tsunamis, droughts, forest fires, flooding, and landslides. The central government and regional administrations have developed numerous disaster management policies, including earthquake disaster management policies. This study's objective is to assess the efficacy of Indonesia's earthquake management policies. This study employed a qualitative research methodology with a descriptive approach. The results of the study indicate that Indonesia's seismic disaster management policies have been comprehensively and systematically formulated through Law Number 24 of 2007 concerning Disaster Management and its derivatives. Government Regulation Number 21 of 2008 Regarding Disaster Management Implementation. At the stage of implementing earthquake disaster management policies in Indonesia, in general, it has been running effectively. This can be measured from indicators of effective communication, availability of resources, disposition and bureaucratic structure. However, even though it has been running effectively, improvements are still needed, such as in terms of information, infrastructure, budget, coordination between the central and regional governments. From this study it can be concluded that to increase the effectiveness of earthquake management policies, the government can increase access to information, infrastructure and resources for earthquake disaster management.

Management of Information and Communication Technology for Earthquake Disaster Management in Jayapura City

This study aims to analyze ICT management in the earthquake disaster in Jayapura Regency. This study uses a qualitative descriptive design. Sources of data come from secondary sources, which include books, journals, online news, and various documents according to the research theme. Based on research results, comprehensive and clear handling of natural disasters has been regulated in Article 5 of Chapter 3 of Law 24/2007 concerning disasters. In ICT management, Badan Nasional Penanggulangan Bencana (BNPB) and Badan Penanggulangan Bencana Daerah (BPBD) use official websites as information channels. Regarding disaster management information and communication systems, BNPB and BPBD themselves have a Disaster Management Operations Control Center, hereinafter abbreviated as Pusdalops PB. The people of Jayapura City mostly access information independently through social media.

A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.

Conducting an adaptive evaluation framework of importance and performance for community-based earthquake disaster management

The frequency and intensity of natural disasters have been increasing in recent decades; especially earthquakes are one of the causes of major natural disasters. Improving community adaptive response to disasters based on community capacity has gradually become an effective means of coping with disaster risks, as well as improving residents' well-being and community participation in disaster planning and management. We integrate community resilience and disaster management to establish an evaluation framework for community-based earthquake disaster management (CEDM) based on community perspectives under importance performance analysis method, and identify the factors affecting community adaptive behavior. Features that affect the differentiation of community residents' adaptive behavior in CEDM program are classified into risk perception, learning earthquake knowledge, the ability on earthquake prevention, and creating a platform on CEDM. The results identify that the CEDM has to integrate the higher community education plan, the stronger flexible to disaster preparedness, the higher the residents' awareness of disaster prevention, the stronger the ability to adapt to disasters; the higher the ability of the government and non-profit organizations to cooperate with the community to deal with disasters, and the stronger the community's ability to manage disasters. These findings provide valuable insights into the construction of CEDM systems and related policymaking.

Earthquake Risk Assessment of Sabah, Malaysia Based on Geospatial Approach

Sabah is located in the northeast region of East Malaysia and recognized as the most active seismic area in Malaysia. The scalability and frequency of earthquakes are growing due to the existence of both local and distant ground motions from active faults, with more than 67 earthquake occurrences with light to moderate magnitude (Mw larger than 3.5)recorded since the 1900. On the other hand, the skewed socio-economic development process associated with the rapid population growth and changes in the family structure, inequality issues, and the lack of adaptation measures would intensify the vulnerability of the earthquakes. Key elements linked to socio-economic vulnerability need to be addressed in order to reduce the risk of earthquake. Based on previous studies, we identified vulnerabilities from a multi-dimensional perspective consisting of exposure, resilience and capacity across districts. Subsequently, a holistic indicator system with 18 variables was constructed to assess the potential earthquake vulnerability in Sabah, Malaysia. The accumulated data will present an earthquake vulnerability classification using the Geographical Information System (GIS) approach. Finally, the earthquake risk was derived by integrating the earthquake vulnerability map with earthquake hazard map proposed by the Department of Mineral and Geoscience (JMG) Malaysia. The results of the analysis revealed that the highest level of earthquake risk, which accounts for 15.5 %, were concentrated in the eastern part of the Sabah region; the high-risk areas account for 7.7 %; the moderate-risk areas account for 11.3 %; and the low to very low risk areas account for 65.4 %. Accordingly, it is expected that the derived earthquake vulnerability and risk map will allow the policymakers and response teams to improve the earthquake disaster mitigation and management in Sabah.

Multi-Hazard Emergency Response for Geological Hazards Amid the Evolving COVID-19 Pandemic: Good Practices and Lessons Learned from Earthquake Disaster Management in Greece

Since the beginning of 2020, the COVID-19 pandemic has caused unprecedented global disruption with considerable impact on human activities. However, natural hazards and related disasters do not wait for SARS-CoV-2 to vanish, resulting in the emergence of many conflicting issues between earthquake emergency response actions and pandemic mitigation measures. In this study, these conflicting issues are highlighted through the cases of four earthquakes that struck Greece at different phases of the pandemic. The earthquake effects on the local population and on the natural environment and building stock form ideal conditions for local COVID-19 outbreaks in earthquake-affected communities. However, the implementation of response actions and mitigation measures in light of a multi-hazard approach to disaster risk reduction and disaster risk management has led not only to the maintenance of pre-existing low viral load in the earthquake-affected areas, but in some cases even to their reduction. This fact suggests that the applied measures are good practice and an important lesson for improving disaster management in the future. Taking into account the aforementioned, a series of actions are proposed for the effective management of the impact of a geological hazard in the midst of an evolving biological hazard with epidemiological characteristics similar to the COVID-19 pandemic.

Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey

Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone to earthquakes, however, there have been no earthquakes in those areas recently. This research proposes and tests the ability of the k-mean clustering method to create the training dataset for earthquake vulnerability analysis. Subsequently, the derived sample dataset was used in four state-of-the-art models i.e. Decision Tree (DT), Support Vector Machine (SVM), Self-Organizing Map (SOM) and Logistic Regression (LR) for assessing earthquake vulnerability in Istanbul, Turkey. The multicollinearity among the variables was determined using tolerance (TOL) and variance inflation factor (VIF) which revealed no multicollinearity among the variables. The highest VIF belonged to the “distance to faults” factor. Vulnerability related variables were classified, weighed and using k-mean clustering, a training database was constructed. Then, the standardized variables were keyed in as input alongside the training site maps into DT, SVM, SOM and LR to construct an Earthquake Vulnerability Map (EVM). EVMs were created for all the four samples and graded as very-low, relatively-low, moderate, high, or extremely-high. Several statistical metrics such as Area under the ROC curve (AUC), sensitivity (SST), specificity (SPF), root-mean-squared-errors (RMSE), positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the accuracy of the resultant maps. The highest and lowest AUC prediction rates were 0.962 and 0.912 from the K-means-SOM and K-means-LR models, respectively. The lowest RSME results using the testing dataset (0.329) belonged to K-means-SVM model. The region's most prone vulnerability maps were found to be in the districts 9, 13, 20, 21 and 35. Finally, an analysis of the buildings and population distribution was carried out among the 39 districts of Istanbul considering the SOM outcomes. The research outcome could help in laying strategies for earthquake preparedness in the Istanbul city.

Activity Analysis of the Fuyu North Fault, China: Evidence from the Time-Series InSAR, GNSS, Seismic Reflection Profile, and Plate Dynamics

Abstract Earthquake disasters are frequent, and the seismic intensity is large in Northeast China. Earthquake activity research is an important aspect of earthquake disaster management. We chose some unconventional means to study fault activity, to find updated activity evidence. The Ms 5.3 earthquake occurred near the Fuyu North Fault (FNF) of China on May 27, 2018. Using the Sentinel-1B descending orbit data from 2016 to 2019, the line-of-sight (LOS) surface deformation in the study area was calculated by using the small baseline subset (SBAS) method. After transforming to the horizontal EW deformation, the variance component estimation method was used for fusion reconstruction with the EW data of the surrounding GNSS stations. The polynomial least square method is used to fit the fault slip rate of three EW data on the surface trace of the FNF. The fitting results of the three regions show that the horizontal eastward distribution rate of the upper plate is significantly greater than that of the lower plate, which is left-lateral clockwise torsion. The vertical structural deformation caused by the growth strata of the upper and lower plates of the upper SYT2 seismic profile of the FNF is quantitatively calculated, and the thrust rate of the upper plate is 0.2 mm/y relative to that of the lower plate. Based on the Li Siguang chessboard structure model, we found that the compression stress in the north-south direction is gradually weakened, and the compression stress in the east-west direction is gradually enhanced. Through the Coulomb stress analysis, the three events of CMT only induced the historical focal location of the surrounding part. The events of 2017 did not induce the events of 2018, but the events of 2019 were related to the induced effects of 2017 and 2018.

The synergy between government institutions in clean water preparedness for earthquake and tsunami disaster management in Banda Aceh City

Synergy is coordination and cooperation between groups of people in order to achieve an efficient goal. An example of a synergy is the synergy between government agencies to improve access to clean water in relation to disaster management. This study aims to describe the synergy between Indonesia’s government agencies to improve access to clean water in relation to disaster management, determine the factors that affect the synergy, and recommend a synergy model. The study was conducted through a qualitative approach. Data were collected through in-depth interview with the heads of government agencies that had a duty to improve access to clean water, observation, and literature review. The data analysis technique was triangulation technique. The results of this study was that the synergy between the government agencies had not run well due to several inhibiting factors that were inefficient planning, overlapping work division, and no evaluation.

Invisible Resilience: Indigenous Knowledge Systems of Earthquake Disaster Management in Kagera Region, Tanzania

Abstract Indigenous environmental disaster management systems are maintained in remotely located communities through intergenerational transmission. This fieldwork illuminates a wealth of unprecedented survival techniques, strategies and skills for offsetting the worst effects of a natural disaster in earthquake-prone areas of north-west Tanzania. Normally, responses from the government and its foreign development partners are hamstrung by bureaucratic red tape, taking too long in reaching disaster victims to be of any actual help. The formal mechanisms of global assistance constitute disaster management failure by design. Rather, it is the local experts who sustain human lives in the weeks and months before external aid comes to the rescue. Yet local communities’ contributions to their own survival remain invisible to central government and the global arena. Traditional means of forecasting environmental catastrophes and of providing essential assistance in the aftermath of natural disasters are reflections of cultural values, socio-economic sophistication and scientific expertise within communities whose resilience needs to be recognized, assisted and promoted. Educational curricula of the future, involving a new generation of academicians, should integrate this crucial indigenous knowledge into the nation’s mainstream disaster management framework.