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This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

In the event of a disaster, educational institutions like schools serve as lifeline buildings. Hence, it is crucial to safeguard these buildings for the communities that may depend on the school as a disaster shelter and aid center. Thus, this paper aims to conduct a multihazard risk assessment survey at 50 schools (with 246 building blocks) in Dehradun.

The past few decades have witnessed the impact of multihazard frequency in Uttarakhand, India, due to the geographical features of the Himalayas and its neo-tectonic mountain-building process. Dehradun is the capital of Uttarakhand state and comes under seismic zone IV, which is highly prone to earthquakes.

The hazard assessment is divided into two types of surveys: first, building-level surveys that include rapid visual screening, nonstructural risk assessment and fire safety audit, and second, campus-level surveys that include vulnerability analysis for earthquake, flood, industrial hazard, landslide and wind.

This paper will list several gaps and unrecognized practices in the region that increase the schools’ multihazard risk. The study’s outcome will help prioritize the planning of disaster awareness, retrofitting execution, future construction practices and decision-making to minimize the risk and prepare the school for the upcoming disasters.

Physical data were collected by the author to determine the multihazard risk analysis in 50 schools in the Dehradun District of Uttarakhand, India. The building- and campus-level surveys have been used to generate a database for the retrofit and renovation process for each individual school to use their budget fruitfully and in a planned way. The survey conducted is more effort and a more detailed risk evaluation which necessitates effectively mitigating and ensuring the potential safety of the region’s schools.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.

Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

This study aims to identify the regions with a high risk of natural disaster, estimate the proportion of households potentially participating in natural disaster insurance and analyze the relationship between disaster risk index and proportion of household potentially participating in natural disaster insurance.

Descriptive and quadrant analysis was applied on the 2019 Indonesia Disaster Risk Index (IRBI) scores and the 2019 National Socio-Economic Survey data.

The results showed there are only two categories of disaster risks in Indonesia based on IRBI categorization: “Medium” and “High.” Some districts in Aceh Province such as Simeuleu, Pidie Jaya and Banda Aceh City were observed to have a high proportion of households potentially participating in the natural disaster insurance while some in Jawa Tengah provinces have fairly low level even though they were categorized as high disaster-prone areas. Moreover, the quadrant analysis showed that 43 districts have high IRBI scores but low insurance participation rates with most discovered to be in Jawa Barat and Sumatera Selatan provinces.

Indonesia does not have a financial mitigation program up to the present time because almost all disaster resolutions are formulated based on emergency funds from the state budget even though it is important to use insurance schemes in all stages of disaster management. To the best of the authors’ knowledge, this study is the first attempt to identify households potentially participating in natural disaster insurance through the National Socio-Economic Survey in Indonesia.

The purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.

In this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.

It was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.

The research is original. None of the parts of this research paper has been published anywhere.