MiMapper: A Cloud-Based Multi-Hazard Mapping Tool for Nepal

Land use planning is a comprehensive master plan for any sustainable development action plan and can integrate all land resources and natural hazards management alternatives (strategies, plans and scenarios). This can be done via a multi-attribute decision method such as Analytical Hierarchy Process (AHP) under data and decision uncertainty. In this paper, fi rst, the potential land degradation hazard was mapped at 1:250,000 scale as four hazard classes. This was done in the context of 14 micro (unit) and six macro (type) physiographic units, regarding the synergetic effects of fi ve key processes (salinization, ponding, water erosion, wind erosion, and vegetation deterioration). A fi ve-class numerical subjective model was used. Secondly, with respect to the nature of land degradation mechanism and intensity in the Gorgan coastal plain (a complex mosaic for land use planning and natural hazard management) four hazard mitigation plans were proposed These were aligned with the goals of national combating of desertifi cation. These include: (1) Drainage and surface fl ow water collection, (2) shrub plantation and green belt creation, (3) soil physicochemical improvement, and (4) protection and preservation. Finally, the relative weights and priorities of management plan alternatives were determined in each hazard class zone. This was accomplished with respect to the hazard intensity and plan implementation cost and effectiveness using AHP model (CR=0.04 for plans, and CR=0.03 for hazard classes). The results indicate land degradation hazard classes I, II, III and IV which are extended with a spatially ordered pattern from forest covered mountain in the south to steppic coastal plain in the north along a sharp geo-ecological gradient of the study area. The differences of hazard class weights and surface areas, and the priority weights of the plans are signifi cant at 0.01 and 0.05 level, respectively. These differences lead to different possible alternatives for hazard management plans and decision making.

Kolkata being a metropolitan city in India has its main municipal solid waste dumpsite situated at Dhapa just adjacent to the East Kolkata Wetlands (Ramsar site). The current prevalent situation at Dhapa is open dumping leading to various contaminations and hazards putting forth the need to look for alternative sites where the landfiilling operation can be shifted to using scientific methods. A user interface (UI)-based analytical hierarchy process (AHP) tool has been developed within the Google Earth Engine (GEE) cloud platform to find out the alternative dumping sites using geospatial layers. AHP function is not available as a native algorithm or developed by any researcher in GEE. The tool has three major functionalities, of which the first one handles the UI elements. The AHP procedure is within another function, and the last function integrates the AHP coefficients to the layers generating the final suitability layer. Users can also upload comparison matrix as GEE asset in the form ofCSV file which gets automatically integrated into the AHP to calculate the coefficients and consistency ratio to generate the spatial suitability layers. This approach showcases a generalized AHP function within the GEE environment, which has been done for the first time. The tool is designed in the cloud platform which is dynamic, robust and suitable for use in various AHP-based suitability analysis in environmental monitoring and assessment.

Population growth, urbanization, policy changes, economic activities, agriculture, infrastructure development, and climate change are some of the factors that can lead to land cover changes. This necessitates serious monitoring to determine the extent of land changes occurring. Semarang City is one of the cities that has undergone significant land changes. This can be seen from the substantial areas that have undergone land-use conversion. This study aims to observe land cover changes in Semarang City using Landsat 8 TOA satellite imagery analyzed through the Google Earth Engine (GEE) platform. GEE is an alternative for image processing as it simplifies the process of image analysis compared to conventional desktop-based image processing methods. The classification is performed using a machine learning algorithm with the Classification and Regression Trees (CART) method tha available in GEE. The accuracy of the classification is tested using the confusion matrix calculation. The results obtained show the accuracy of land cover change testing for the years 2013, 2016, 2019, and 2022, with kappa accuracies reaching 96.7%, 93.78%, 94.54%, and 96.04%, respectively. The effectiveness of image processing on the GEE platform shows that GEE can be used as a fast and efficient alternative for image processing. Based on the research findings, it is shown that residential areas experience significant increases every year. Therefore, the increase in residential areas has a significant impact on the surrounding environment, such as increasing urban temperatures, which reduces the comfort level of residents, especially in Semarang City. Keywords : Land Cover, Landsat 8 Satellite Imagery, Google Earth Engine ABSTRAK Pertumbuhan populasi, urbanisasi, perubahan kebijakan, aktivitas ekonomi, pertanian, pengembangan infrastruktur, dan perubahan iklim adalah beberapa faktor yang dapat menyebabkan perubahan tutupan lahan. Hal ini memerlukan pemantauan yang serius untuk melihat seberapa besar perubahan lahan yang terjadi. Kota Semarang merupakan salah satu kota yang telah banyak mengalami perubahan lahan. Hal ini dapat dilihat dari banyaknya wilayah yang telah beralih fungsi lahan. Penelitian ini bertujuan untuk melihat perubahan tutupan lahan di Kota Semarang dengan menggunakan data citra satelit Landsat 8 TOA yang di analisis menggunakan platform Google Earth Engine (GEE). GEE menjadi alternatif pengolahan citra karena memudahkan pengguna dalam melakukan pengolahan dan analisis citra dibandingkan dengan metode konvensional pengolahan citra berbasis desktop. Klasifikasi dilakukan dengan algoritma machine learning menggunakan metode Classification and Regression Trees (CART) yang tersedia di GEE. Uji akurasi klasifikasi dilakukan dengan menggunakan perhitungan confusion matrix. Hasilnya diperoleh uji akurasi perubahan tutupan lahan pada tahun 2013, 2016, 2019 dan 2022, akurasi kappa masing-masing mencapai 96,7%, 93,78%, 94,54%, dan 96,04%. Efektifitas pengolahan citra di platform GEE menunjukkan bahwa GEE dapat digunakan sebagai alternatif dalam pengolahan citra yang cepat dan efisien. Berdasarkan hasil penelitian menunjukkan bahwa wilayah pemukiman mengalami kenaikan yang signifikan setiap tahun Oleh karena itu, peningkatan pemukiman memberi dampak yang signifikan terhadap lingkungan sekitar, seperti peningkatan suhu kota, yang menyebabkan tingkat kenyamanan penduduk semakin berkurang, terutama di Kota Semarang. Keywords : Tutupan Lahan, Citra Satelit Landsat 8, Google Earth Engine.

Integrating Geographic Information Systems (GIS) with Building Information Modeling (BIM) has become a potent strategy for handling the complexity of contemporary facility management, infrastructure development and urban planning. GIS is superior at geographical analysis and large-scale environmental context however, BIM is largely focused on precise building design and lifecycle management. The amalgamation of these two technologies facilitates stakeholders in thoroughly comprehending project elements at both the micro and macro levels. On the other hand, GIS offers extensive spatial datasets encompassing geographical, environmental, and topographical information. By integrating BIM and GIS users can analyze the building's interaction with their surrounding environments, optimize site selection, enhance project planning and ensure better sustainability outcomes. BIM offers precise 3D models enhanced with metadata for building components, facilitating accuracy in construction and maintenance. Improved decision-making, expedited workflows and more cross-disciplinary cooperation are just a few advantages of this synergy. Applications include facility management, smart city planning, urban infrastructure development and disaster management. However, there are still issues with technology integration, standards alignment and data interoperability. However, the combination of BIM and GIS has enormous potential to influence future infrastructure projects and cities provided that data transmission formats and software capabilities continue to progress.

Earth fissures caused by tectonic forces, human activities, or both seriously threaten the safety of people’s lives and properties. The Taiyuan Basin, a Cenozoic downfaulted basin located in the centre of the Fen-Wei Basin tectonic belt, in northwestern China, presents the ideal study area for a hazard assessment of earth fissures. A total of 104 earth fissures have been observed in the Taiyuan Basin, with a total length of approximately 128 km. In this paper, we proposed a probabilistic method for mapping earth fissure hazards by integrating the analytic hierarchy process (AHP), the area under the curve (AUC), and the certainty factor model (CFM). Geomorphic units, geologic formations, active faults and land subsidence zones of the Taiyuan Basin were mapped in detail. Correlations between these factors and earth fissures were evaluated through spatial modelling in ArcGIS. The AUC was introduced into the AHP to weight each factor and thus, to derive an earth fissure susceptibility map. Finally, the modelled earth fissure susceptibility was compared with a digital inventory of earth fissures to develop a probability function and map the spatial variability in failure probability through the CFM. The study indicates that active faults have the greatest contribution to the generation of earth fissures. Earth fissures are prone to develop in the piedmont alluvial-diluvial clinoplain and the transitional zone near the geomorphic boundary. This mapping procedure can assist in making rational decisions regarding urban planning and infrastructure development in areas susceptible to earth fissures.

Natural disasters can have significant widespread impacts on society, and they often lead to loss of electric power for a large number of customers in the most heavily impacted areas. In the United States, severe weather and climate events have been the leading cause of major outages (i.e., more than 50,000 customers affected), leading to significant socioeconomic losses. Natural disaster impacts can be modeled and probabilistically predicted prior to the occurrence of the extreme event, although the accuracy of the predictive models will vary across different types of disasters. These predictions can help utilities plan for and respond to extreme weather and climate events, helping them better balance the costs of disaster responses with the need to restore power quickly. This, in turn, helps society recover from natural disasters such as storms, hurricanes, and earthquakes more efficiently. Modern Bayesian methods may provide an avenue to further improve the prediction of extreme event impacts by allowing first-principles structural reliability models to be integrated with field-observed failure data. Climate change and climate nonstationarity pose challenges for natural hazards risk assessment, especially for hydrometeorological hazards such as tropical cyclones and floods, although the link between these types of hazards and climate change remains highly uncertain and the topic of many research efforts. A sensitivity-based approach can be taken to understand the potential impacts of climate change-induced alterations in natural hazards such as hurricanes. This approach gives an estimate of the impacts of different potential changes in hazard characteristics, such as hurricane frequency, intensity, and landfall location, on the power system, should they occur. Further research is needed to better understand and probabilistically characterize the relationship between climate change and hurricane intensity, frequency, and landfall location, and to extend the framework to other types of hydroclimatological events. Underlying the reliability of power systems in the United States is a diverse set of regulations, policies, and rules governing electric power system reliability. An overview of these regulations and the challenges associated with current U.S. regulatory structure is provided. Specifically, high-impact, low-frequency events such as hurricanes are handled differently in the regulatory structure; there is a lack of consistency between bulk power and the distribution system in terms of how their reliability is regulated. Moreover, the definition of reliability used by the North American Reliability Corporation (NERC) is at odds with generally accepted definitions of reliability in the broader reliability engineering community. Improvements in the regulatory structure may have substantial benefit to power system customers, though changes are difficult to realize. Overall, broader implications are raised for modeling other types of natural hazards. Some of the key takeaway messages are the following: (1) the impacts natural hazard on infrastructure can be modeled with reasonable accuracy given sufficient data and modern risk analysis methods; (2) there are substantial data on the impacts of some types of natural hazards on infrastructure; and (3) appropriate regulatory frameworks are needed to help translate modeling advances and insights into decreased impacts of natural hazards on infrastructure systems.

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Natural Hazard Science. Please check back later for the full article. Spatial and urban planning are acknowledged as important tools and processes that influence exposure to natural and technical hazards and risk accumulation, as well as risk and vulnerability reduction. Even though natural hazards (such as floods) and technical hazards have been discussed in spatial and urban planning for quite some time in various countries and regions, only in a very few cities and regions has there been a sufficient and systematic approach to establish risk management as part of the planning task within the field of spatial planning and urban land-use planning. Risk management strategies in spatial and urban planning have often been strengthened after major crises, such as severe fires in the middle ages in cities in Europe, or after major floods or hurricanes in North America, Asia, and Latin America, as well as Europe and Africa. In this context, risk management is understood as a cluster of concrete and practical strategies and actions on how to handle risks, and in terms of spatial and urban planning, including those risks that are of spatial importance or significant with regard to planning processes.

Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases’ spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the “VGICED Atlas”, a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps.

Disaster risks and disaster management policies and practices in Pakistan: A critical analysis of Disaster Management Act 2010 of Pakistan

Geographically, Indonesia is located in southeast Asia between the Indian and the Pacific Oceans. It is recognized as an active tectonic region because it consists of three major active tectonic plates: the Eurasian plate in the north, the Indo-Australian plate in the south, and the Pacific plate in the east. The southern and eastern parts of the country feature a volcanic arc stretching from the islands of Sumatra, Java, Nusa Tenggara, and Sulawesi, while the remainder of the region comprises old volcanic mountains and lowlands partly dominated by marshes. Territorially, it is located in a tropical climate area, with its two seasons—wet and dry—exhibiting characteristic weather changes, such as with regard to temperature and wind direction, that can be quite extreme. These climatic conditions combine with the region’s relatively diverse surface and rock topographies to provide fertile soil conditions. Conversely, the same conditions can lead to negative outcomes for this densely populated country, in particular, the occurrence of hydrometeorological disasters such as floods, landslides, forest fires, and drought. The 2017 World Risk Report’s ranking of countries’ relative vulnerability and exposure to natural hazards such as earthquakes, storms, floods, droughts, and sea-level rise calculated Indonesia to be the 33rd most at-risk country. Between 1815 and 2018, 23,250 natural hazards occurred here; 302,849 people died or were otherwise lost, 371,059 were injured, and there were 39,514,636 displaced persons, as well as billions of rupiah in losses. The most frequent type of natural hazard has been floods (8,919 instances), followed by cyclones (5,984), and then landslides (4,947). Following these latest disasters and acknowledging that Indonesia is becoming increasingly vulnerable to such natural hazards, the country’s government established a comprehensive disaster management system. Specifically, it instituted an organization capable of and responsible for handling such a wide-reaching and complex situation as a natural hazard. A coordinated national body had first been developed in 1966, but the current discourse concerning proactive disaster risk management at national and local levels has encouraged the central government to adapt this organization toward becoming more accountable to and involving the participation of local communities. Law No. 24/2007 of the Republic of Indonesia Concerning Disaster Management, issued on April 26, 2007, established a new National Disaster Management Agency (BNPB), but it also focusses on community-based disaster risk management pre- and post-disaster. Through the BNPB and by executing legislative reform to implement recommendations from the international disaster response laws, Indonesia has become a global leader in legal preparedness for natural hazards and the reduction of human vulnerability.

Flooding is considered a significant natural hazard in Malaysia. climatic conditions and anthropogenic activities are gradually triggering floods in different parts of Malaysia including the study area, i.e. Shah Alam municipality. In the southern region of Shah Alam, where the Klang River runs, November/December and March/April saw the most flooding. To create a mapping of Shah Alam’s flood potential, the Analytic Hierarchy Process (AHP) and Fuzzy-AHP methods were applied with the twelve criteria. The Sentinel 1 Synthetic Aperture Radar (SAR) datasets and the Google Earth Engine (GEE) platform were used to compute the flood inundation area. Based on the twelve criteria, flood potential zones were divided into five categories such as very high potential (11.58 km2 – AHP and 10.35 km2 – F-AHP) to very low potential (6.49 km2 – AHP and 39.21 km2 – F-AHP), respectively. The most affected areas are the southern part (near Klang River), the central part, and some parts of the northern zone in Shah Alam. The near real-time flood mapping used for previous flood-affected area identification in Shah Alam, Malaysia. Local government and relevant stakeholders can benefit from using this flood potential mapping to reduce the flood effects at Shah Alam via appropriate planning.

The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

On the emergence of geospatial cloud-based platforms for disaster risk management: A global scientometric review of google earth engine applications

Currently, natural hazards are a significant concern as they contribute to increased vulnerability, environmental degradation, and loss of life, among other consequences. Climate change and human activities are key factors that contribute to various natural hazards such as floods, landslides, droughts, and deforestation. Assam state in India experiences annual floods that significantly impact the local environment. In 2022, the flooding affected approximately 1.9 million people and 2930 villages, resulting in the loss of 54 lives. This study utilized the Google Earth Engine (GEE) cloud-computing platform to investigate the extent of flood inundation and deforestation, analyzing pre-flood and post-flood C band Sentinel-1 GRD datasets. Identifying pre- and post-flood areas was conducted using Landsat 8–9 OLI/TIRS datasets and the modified and normalized difference water index (MNDWI). The districts of Cachar, Kokrajhar, Jorhat, Kamrup, and Dhubri were the most affected by floods and deforestation. The 2022 Assam flood encompassed approximately 24,507.27 km2 of vegetation loss and 33,902.49 km2 of flood inundation out of a total area of 78,438 km2. The most affected areas were the riverine regions, the capital city Dispur, Guwahati, southern parts of Assam, and certain eastern regions. Flood hazards exacerbate environmental degradation and deforestation, making satellite-based information crucial for hazard and disaster management solutions. The findings of this research can contribute to raising awareness, planning, and implementing future disaster management strategies to protect both the environment and human life.

The goverment gives urban villages more power includes urban village development. In accordance with local village goverment policies, there are specific criteria for allocating funds for village infrastructure development. The aim is to make the development of urban village infrastructure more equitable and targeted. Priorities for urban village infrastructure development must be decided. Decisions on urban infrastructure development are still made by voting and voting and often more significant developments have to be postponed due to losing votes. To prioritizes urban infrastructure development, urban village officials can use decision support system. Prioritization of urban village infrastructure development is determined using Simple Additive Weight (SAW) and Product weightn(WP) methodologies. Each proposal will be assessed according to the criteria chosen by the Kelurahan to determine development priorities. It is expected that the decision support system will be easier, more accurate, and faster in determining development priorities in Rangkapan jaya urban village. Comparison of SAW and WP methods using 10 alternative data, shows that both methods get accurate data and are suitable when applied as ranking of infrastructure development.