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This paper aims to provide a general overview of the international Tsunami warning system mandated by the United Nations, particularly on cataloging past studies and a strategic focus in the Indian Ocean, particularly on the Bay of Bengal region.

Present research assimilates the secondary non-classified data on the Tsunami warning system installed in the Indian Ocean. Qualitative review and exploratory research methodology have been followed to provide a holistic profile of the Tsunami rarly warning system (TEWS) and its role in coastal resilience.

The study finds the need for strategic focus to expand and interlink regional early warning cooperation mechanisms and partnerships to enhance capacities through cooperation and international assistance and mobilize resources necessary to maintain the TEWS in the Indian Ocean region. The enhanced capacity of the TEWS certainly improves the resilience of Indian Ocean coastal communities and infrastructures.

The study is original research and useful for policy planning and regional cooperation on data interlinkages for effective TEWS in the Indian Ocean region.

The analytical framework proposed in this study aims to link the capital portfolio approach to sustaining human well-being, 2015 sustainable development goals and development action ARC-D concepts. Nepal case study is a “tribrid” power generation system that combines distributed solar, hydro and wind power generation capacities for the resilience of a community of around 500 people in a remote village with a total installed capacity of 28 kW. The second case study is about the solarization of 900 health centres in Chhattisgarh, India, with off-grid solar PV with a cumulative capacity of 3 MW.

Critical infrastructure at the community scale needs to be resilient to maintain community-level functionality in the face of adverse impacts. The present study provides two case study sites from Nepal and India to demonstrate various elements of resilience building for critical infrastructures, especially for the energy sector.

Granular technology and distributed generation in Nepal and India can act as critical infrastructure in providing on-demand electricity service to enhance community-level resilience along with future opportunities of scale up.

The analytical framework for evaluating community-scale resilience through critical infrastructure design and application of the framework using evidence based on case studies are the original contributions.

The purpose of this study is to understand through the qualitative assessment, how the current strategy plans are geared toward reducing urban flood risks and achieving Sustainable Development Goals (SDGs) 11 and 13.

The Bangkok Metropolitan Region (BMR) plays a major role in Thailand’s economic development. Thus, when the 2011 Thailand flood disaster occurred, BMR suffered major economic and social losses, which impacted the rest of the country. This mega disaster prompted policymakers, the academe and other relevant stakeholders to reevaluate and amend the current urban flood risk reduction measures and governance. The present study attempts to evaluate and compare the post-2011 Thailand flood disaster strategy and master plans, policies and reports that directly and indirectly reduce urban flood risks in the provinces of BMR. Basing on SDGs 11 and 13 targets that impact urban flood risk and resilience, a set of criteria was developed to screen, score and asses the selected documents. A screening process of three levels are conducted to limit the documents to be reviewed, and subsequent content analysis for scoring also has been done.

The projected results indicate the need for improved and increased number of localized strategic plans and policies, which are more comprehensive and integrated as risk governance documents.

Furthermore, it is projected that there is need to integrate measures to increase adaptive capacity for BMR.

This study is original, and methodology can be replicated for other urban areas for flood risks and resilience assessment.

The effects of population growth in the developing world and climate change have increased the stress on available water resources. The majority of Rajshahi city, Bangladesh, is facilitated with groundwater withdrawal. As Bangladesh is a country of monsoon climate, reserved rainwater can be contributed as an alternative to extracted groundwater. This study aims to develop a framework for rooftop rainwater harvesting (RRWH) for domestic purposes and estimate the appropriate size of the storage tanks and their costs required to fulfill the annual drinking and cooking water demands through RRWH in Rajshahi city of Bangladesh.

A total of 100 single-story residential dwellings with varying rooftop areas were surveyed for the projection of RRWH potential. The relationship between the size and cost of a water tank and the rooftop areas of different houses is expressed using a general mathematical equation. Cost estimates for the proposed RRWH system for all houses have been completed, and a cost model illustrating the relationship between rooftop or catchment area and associated cost of RRWH system has been developed.

This study reveals that a maximum of 110.75 m³/year rainwater can be collected from a 100 m² rooftop area of Rajshahi city. Moreover, this study finds that such harvesting of rainwater can reduce municipal water supply to the extent of almost 75%. Water samples collected from rooftops also revealed that if germs were removed through bacteria treatment, the collected rainwater potentially can be used for drinking and cooking purposes.

The novelty of this study is that it focused mainly on how significant RRWH can be to meet people’s daily required amount of water for household purpose and ascertain the cost reduction using the RWH method. This paper also is unique as it assessed the volume of the storage tank that is sufficient to distribute the necessary amount of water for drinking and cooking purpose as a sustainable alternative source in the dry season.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

To minimize risk of coastal communities arising from cyclones, several risk mitigation initiatives have been taken in countries. Cyclone shelters have proven to be an important critical infrastructure in saving lives from cyclones. A large number of coastal critical infrastructure in the form of multi-purpose cyclone shelters (MPCS) are built to provide safe shelter during disasters. Often observed, such critical infrastructures are non-operational during the normal period, which makes them difficult to use during any disaster. Efforts have been made to keep these infrastructures in working condition. This research paper aims to bring together various management practices adopted for the MPCS in the South-Asian region with a focus on Bangladesh, and India. It also suggests ways to improve these practices for sustainable management of the MPCS.

India and Bangladesh are the most vulnerable countries in the South Asian region. As per the Global Climate Index, India and Bangladesh come in the list of “in extreme risk” countries in the world and are vulnerable to several natural hazards, especially climate-induced hydrometeorological hazards. India has a vast coastline and out of 7,516 km of coastline, a large extent, i.e. 5,700 km is prone to cyclones and that keeps 40% of the population vulnerable living within 100 km of the coastline. On the other hand, Bangladesh has a coastline of 580 km, which is equally vulnerable to tropical cyclones. Safeguarding communities from impending coastal risk through coastal cyclone shelters are of prime concern. This paper uses a qualitative approach to analyze secondary data, and literature in the field of critical infrastructure, sustainability, cyclone shelter, and management practice for cyclone shelters.

To provide sustainability and community ownership of the MPCS, various service plans are adopted in different countries. This paper provides insights on service and sustainability efforts made for the proper functioning of the MPCS in India and Bangladesh. It also provides insight into the roles played by different institutions involved in maintaining the MPCSs.

The research reiterates understanding of the cyclone shelter management from different geographic locations in the South Asian region. Various gaps identified in shelter management practices are discussed in the paper and key recommendations are proposed for better management of cyclone shelters.

The purpose of this paper is to assess the public health risk factors of internally displaced households and suggest appropriate measures and strategies for health risk reduction in the context of IDPs.

The composite Index (CI) method was used to compare the public health risk factors at the household level in three IDP camps. A set of 22 indicators were studied in 326 households. Households were selected by using a two-stage cluster sampling technique.

The findings indicate that the Shangil Tobaya camp is at the highest risk for communicable diseases (63.6%) followed by Zamzam (52.4%) and Abu Shouk (42.7%) at the household level. Eight indicators appeared to have made differential impacts between Abu Shouk and Shangil Tobaya, these include: level of education, walking time to health facilities, water source, latrines type, safe disposal of child feces, frequency of visit by pregnant women to antenatal care services, place of delivery and women delivering their children with the help of skilled birth attendants.

Since the selection criteria of the camps were predefined; there are variations in the number of samples between the camps. Therefore, the generalizability may be compromised.

Increased access to healthcare services particularly reproductive health services to the most vulnerable groups (women). Community involvement in services management to promote ownership.

The methods used in this study is original and flexible and can be replicated for other emergency areas and risks.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

The purpose of this paper is to look into how collaborations among science and technology groups and other stakeholders in the Asia-Pacific region can facilitate implementation of national and local disaster risk reduction (DRR) strategies through education, training and field practice. Case studies on transdisciplinary approach (TDA) that integrates cross-cutting DRR issues and various stakeholders through science and technology are introduced from several countries in the region as attempts to holistically support societies build and strengthen their disaster resilience.

First, through literature review, the transition from single discipline to TDA is illustrated in which various stakeholders of different disciplines work together to achieve a common societal goal. This is followed by introduction of several case studies of actual TDA implementation for DRR in which information had been gathered through surveys and interviews with international science and technology stakeholders. Finally, through analysis and discussion, the study identifies the key roles of science and technology stakeholders for facilitating TDA for DRR.

The study concludes with key findings on the specific roles of science and technology including provision of education and training to build capacities of DRR practitioners to effectively plan and implement DRR measures, support to evidence-based decision making through locally specific scientific assessments and analysis and validation scientific information on the actual field for DRR practitioners and agencies. In materializing these roles into action, institutionalization of supporting policies and budget provision that promotes TDA are suggested.

Even though TDA is not a new concept and many stakeholders understand its importance, TDA has not been widely exercised mainly due to conventional practices of experts and organizations working only within their groups and disciplines. With this understanding, this study has newly identified eight key elements that can be used as a guide and checklist for DRR stakeholders to effectively implement TDA for strengthening disaster resilience of their societies.

As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.