Spatial Analysis of Flood-Prone Areas in Padang Terap, Kedah: Integrating Spatial Autocorrelation and Optimized Hotspot Analysis

Long-term InSAR techniques, such as Persistent Scatterer Interferometry and Distributed Scatterer Interferometry, are effective approaches able to detect slow-moving landslides with millimeter precision. This study presents a novel approach of optimized hot spot analysis (OHSA) on persistent scatterers (PS) and distributed scatterers (DS), and evaluates its performance on detection of landslides across the Volterra area in central Tuscany region of Italy. 1625 ascending and 2536 descending PS processed from eight years (2003–2010) of ENVISAT images were produced by the PS-InSAR technique. In addition, 16,493 ascending and 9746 descending PS/DS measurement points (MP) processed from four years (2011–2014 for ascending orbits and 2010–2013 for descending orbits) of COSMO-SkyMed images were collected by the SqueeSAR approach. The OHSA approach was then implemented on the derived PS and DS through the analysis of incremental spatial autocorrelation and the Getis-Ord Gi* statistics. As a result of OHSA, PS and DS MP that are statistically significant with velocity >|±2| mm/year, p-value < 0.01 and z-score >|±2.58| were recognized as hot spots (HS). Meanwhile, a landslide inventory covering the Volterra area was manually prepared as the reference data for accuracy assessment of landslide detection. The results indicate that, in terms of OHSA-derived ENVISAT HS, the detection accuracy can be improved from 23.3% to 25.3% and from 50.7% to 66.4%, with decreased redundancy from 5.3% to 3.7% and from 5.3% to 2.4%, for ascending and descending orbits, respectively. In addition, for OHSA-derived Cosmo-SkyMed HS, the detection accuracy can be improved from 57.7% to 70.3% and from 73.8% to 81.5%, with decreased redundancy from 3.1% to 1.7% and from 3.4% to 2.1%, for ascending and descending orbits, respectively. Compared to traditional HS analysis such as Persistent Scatterers Interferometry Hot Spot and Cluster Analysis (PSI-HCA), OHSA has the significant advantage that the scale distance used for the Getis-Ord Gi* statistics can be automatically determined by the analysis of incremental spatial autocorrelation and accordingly no manual intervention or additional digital terrain model (DTM) is further needed. The proposed method is very succinct and can be easily implemented in diverse geographic information system (GIS) platforms. To the best of our knowledge, this is the first time that OHSA has been applied to PS and DS.

Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.

The Fagradalsfjall volcano erupted on March 19, 2021, marking its first eruption in nearly 781 years. During the six-month eruption period (March&amp;#8211;September 2021), a total of 90 Sentinel-1 synthetic aperture radar (SAR) images were collected between January and December 2021. These images consisted of 60 frames from the Sentinel-1A satellite and 30 frames from the Sentinel-1B satellite, providing a short temporal baseline of approximately 6 days between interferogram pairs. The dataset was processed using an advanced time-series InSAR technique based on the Improved Combined Scatterers Interferometry with Optimized Point Scatterers (ICOPS) algorithm, which analyzed surface deformation through a combination of Persistent Scatterer (PS) and Distributed Scatterer (DS) points, collectively referred to as Combined Scatterer (CS) points. To refine the analysis, a convolutional neural network (CNN) was applied to evaluate the temporal patterns of the CS points, and the Optimized Hot Spot Analysis (OHSA) method was employed to spatially optimize these points by identifying statistically significant patterns between hot and cold points. In detail, PS points were identified using an amplitude dispersion index threshold of 0.4, in line with standard StaMPS processing procedures. For DS points, a combination of amplitude and phase information was used: the amplitude data helped detect statistically homogeneous pixels (SHPs) through a Generalized Likelihood Ratio (GLR) test, while phase information enabled analysis of spatial and temporal coherence between each interferogram pair. By combining SHP detection with spatial and temporal coherence, the DS points were selected for further analysis as part of the CS point combination. To enhance displacement pattern reliability, a CNN was employed to assess consistency based on correlation coefficients. OHSA, using Getis-Ord Gi* statistics, was then applied to identify statistically significant hot spots by clustering data according to z-scores and p-values, determining the spatial significance of the deformation patterns. Finally, validation of ICOPS results against GNSS measurements around the deformation area demonstrated consistency in observed deformation patterns. The analysis revealed that deformation around the Fagradalsfjall volcano was primarily driven by magma reservoir activity associated with dike intrusion beneath the surface, which was accompanied by increased earthquake events. Seismic activity in the region was visualized through cross-sections of earthquake distributions during the deformation period, providing deeper insights into the volcanic activity.Acknowledgment: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF&amp;#8211;2023R1A2C1007742).

Flood hazards and risk mapping using geospatial technologies in Jimma City, southwestern Ethiopia

Flood hazard and risk assessment using GIS and remote sensing in the case of Ziway Lake watershed, central Main Ethiopian Rift

Global tectonic activities are playing an important role in the occurrences of devastating earthquakes and related long-term changes in the earth's system surface. However, the plate tectonics processes and their interaction with the earth's crust are very much complex, and it is a subject of unending debate. Therefore, tectonism-induced landslide, tsunami, liquefaction, and fire are significant earthquake-related hazards, which have a larger potential and overwhelming impact on life and infrastructural properties throughout the world. In this study, we have emphasized the identification of earthquake hotspot and coldspot zones considering historical earthquake data across the plate boundary of the world. Here, a total of 7773 historical earthquake points were collected as input parameters with three-moment magnitude (Mw) classes (<4.5, 4.5–6.0, and >6.0). Two statistical methods namely hotspot analysis (Getis-Ord GI*) and optimized hotspot analysis were used in the detection of global earthquake hotspot and coldspot zones using the geographic information system (GIS) platform. Hotspot and coldspot zone are identified under 99%, 95%, and 90% confidence levels. Alongside, here we have also discussed the paradigm, evidence of tectonic, and historical earthquakes, and how and why they are formed with the help of the existing theoretical constraints. The result indicates that the Pacific ring of fire, Peru-Chile Trench, and the mid-Atlantic oceanic ridge is fall in the hotspot zones of 99%, 95%, and 90% confidence levels.

This study aims to target the threat and risk of flooding in Sirimau District, Ambon City from a multi-criteria perspective using a Geographic Information System (GIS). Important flood hazard characteristics include land use, elevation, slope, distance from rivers, soil, and rainfall. Two risk factors, namely population density and land use as well as flood hazard characteristics are used in flood risk analysis. Map aggregation procedure for flood risk and hazard analysis uses the Weighted Linear Combination (WLC) approach. The results of the flood hazard at the study site revealed that the flood hazard category was very high and high, namely 12.26%, and the flood hazard category was very low and low, namely 87.84% and only. The results of the flood risk in the research location revealed that the flood risk with very high and high-risk categories was around 17.28%, and very low and flood low-risk categories (82.77%). This is because the Sirimau District is mostly dominated by hilly and mountainous areas.

With increasing numbers of crashes and injuries, understanding traffic accident spatial patterns and identifying blackspots is critical to improve overall road safety. This study aims at detecting blackspots using optimized hot spot analysis (OHSA). Traffic accidents were classified by their participants and severity to explore the relationship between blackspots and different types of accidents. Based on the outputs of incremental spatial autocorrelation, OHSA was then implemented on different types of accidents. Finally, the performance of OHSA in evaluating the road safety level of the proposed RBT index are examined using a binary correlation analysis (i.e., R2 = 0.89). The results show that: (1) The optimal scale distance varies from 0.6 km to 2.8 km and is influenced by the distance of the travel mode. (2) Central cities, with 54.6% of the total accidents, experiences more rigorous challenges regarding traffic safety than satellite cities. (3) There are many types of black spots in vulnerable communities, but in some specific areas, there are only black spots of non-motor vehicle accidents. Considering the practical significance of the above results, policy makers and traffic engineers are expected to give higher attention to central cities and vulnerable communities or prioritize the implementation of relevant optimization measures.

In flood hazard and risk assessment studies, modeling is generally done by examining the hydrometeorological events that have developed by using past datasets. Recently, increasing rainfall per unit time due to climate change may cause flash floods. Hydrographs, which are input to 1D/2D hydrodynamic models, are also likely to change as a result of climate change. Hydrological calculations based on past data may underestimate the predicted values. Therefore, flood risks produced from the results of flood depth and hazard models may also remain at low values. In this study, firstly, a hydrological modeling study was carried out on the streams in Haramdere Basin by using hydrometeorological measurements between 2010-2022 and hydrographs were produced for Q2, Q5, Q10, Q25, Q50, Q100, Q500 and Q1000 returning periods. In mapping studies, river structures, stream geometry, digital surface and terrain model were determined using ground measurements and flight data. Then, flood depth and hazard maps were created with 1D and 2D hydrodynamic models. Economic risk calculation was made using these maps. Then, RCP8.5 scenarios known as having high precipitation anomalies for all climate models included in CMIP6 were re-run in the hydrological model. In this way, flow data were generated for each climate model RCP8.5 scenario. Then, 3 different climate change impacts (worst, medium and best) for Haramidere Basin in regards to flood hazard and risk will be revealed by analyzing the rainfall and runoff extremes produced from the hydrological model for all climate models. In the worst case scenario, the climate model with the highest rainfall and runoff extremes, in the medium case, the average of the values calculated from all climate models and in the best case scenario, the climate model with the lowest extremes will be selected. In this way, climate models specific to this basin will be determined for these 3 different Scenarios. Then, from these selected climate models, coefficients will be determined to be used in hydrological calculations for the effect of climate change on flooding events. Finally, flood risk calculations will be made for these 3 scenarios and the economic value of climate change in terms of flood risk will be quantified by comparing with the flood risk calculated with the measurements between 2010-2022.

Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome.

Floods are among the most devastating natural disasters in the world, claiming more lives and causing more damage to properties than any other natural phenomena. This study specifies flood hazard areas as well as flood risk areas of Gelana River Watershed. It specifically aims to investigate factors that create good conditions for flood hazard, generate flood hazard and risk areas from environmental and socio-economic factors using integration of Multi-Criteria Evaluation (MCE) and Geospatial Techniques. The research was conducted using quantitative research approach. Therefore, slope, elevation, soil type, land use land cover (LULC), and drainage density were the environmental factors developed for the generation of flood hazard. In addition, flood hazard, LULC and population data factors were developed to generate flood risk areas of Gelana river watershed. As the result, flood hazard map reveals 64.68, 1769.48, 1345.38, 244.37, 10.73 square kilometers of Gelana river watershed, is subjected to very high, high, moderate, low and very low flood hazardous respectively. It is revealed that 46.52% of the watershed has very high to high flood risk. The rest 47.20%, 6.24%, and 0.05% of the study area has medium, low and very low flood risk respectively. Therefore, the area incorporated under very high and high hazardous and risk areas are located around the Main River and lower course of the watershed.

Spatial analysis and geoclimatic factors associated with the incidence of acute lymphoblastic leukemia in Iran during 2006–2014: An environmental epidemiological study

Floods driven by extreme precipitation events pose significant risks to communities, infrastructure, and ecosystems, particularly in regions with complex topography, such as Nepal. Probable Maximum Precipitation (PMP) is a critical parameter for assessing flood risk and designing resilient infrastructure. This review examines the methodologies and findings of a study focused on flood risk assessment in the West Rapti River Basin, Nepal, with emphasis on PMP and Probable Maximum Flood (PMF). This study employs hydrometeorological and statistical methods to estimate PMP, utilizes Snyder’s unit hydrograph for PMF calculation, and applies HEC-RAS 2D modeling to map flood hazards, vulnerabilities, and risks. Key findings include PMP estimates of 507 mm and 575 mm, a PMF of 11,211.1 m³/s, and detailed flood risk maps highlighting significant inundation risks in Deukhuri Valley. This review synthesizes the contributions of this study, compares its approaches to global practices, and discusses its implications for flood risk management in Nepal.The study on flood risk assessment in the West Rapti River Basin, Nepal, employs a comprehensive approach to evaluate and map flood hazards, vulnerabilities, and risks. By combining hydrometeorological and statistical methods to estimate Probable Maximum Precipitation (PMP), the research provides crucial insights into extreme precipitation events in the region. The use of Snyder's unit hydrograph for Probable Maximum Flood (PMF) calculation and HEC-RAS 2D modeling for flood hazard mapping demonstrates a robust methodology for assessing flood risks in complex topographical areas. The findings of this study have significant implications for flood risk management in Nepal and similar regions. The PMP estimates of 507 mm and 575 mm, along with the calculated PMF of 11,211.1 m³/s, provide valuable data for designing resilient infrastructure and implementing effective flood mitigation strategies. The detailed flood risk maps highlighting substantial inundation risks in Deukhuri Valley offer critical information for local authorities, urban planners, and policymakers to develop targeted interventions and improve community preparedness. This research contributes to the broader understanding of flood risk assessment methodologies and their application in regions with challenging topography, potentially informing similar studies in other flood-prone areas worldwide. Keywords: Flood risk assessment, West Rapti River Basin, Nepal, Probable Maximum Precipitation (PMP), Snyder's unit hydrograph, Probable Maximum Flood (PMF), HEC-RAS 2D modeling, Flood hazard mapping, Deukhuri Valley, Flood mitigation strategies, Hydrometeorological methods, Statistical analysis, Extreme precipitation events, Flood risk management, Inundation risks

Accurate economic loss assessment for natural hazards is vital for planning, mitigation, and actuarial purposes. The widespread and costly nature of flood hazards, with the economically disadvantaged disproportionately victimized, makes flood risk assessment and mitigation particularly important. Recent studies have made advancements in assessing flood risk at individual building level. But these methods require solving an integral equation every time the analysis is run, which is computationally intensive. In addition, analyses to date do not consider insurance (i.e., coverage, deductible) information and thus do not estimate the portion of risk that homeowner and insurer bear. Although existing research does consider the freeboard mitigation option for a building, the role of other types of hazard mitigations, especially in coastal areas, in reducing flood risk reduction is not well explored. This dissertation develops advanced analytical methods to quantify homeowner’s flood risk considering insurance information and hazard mitigation options. The flood hazard is characterized using a two-parameter (i.e., location and scale parameter) Gumbel extreme value distribution, with flood risk quantified in terms of average annual loss (AAL) at the individual building level. Monte Carlo simulation is used to estimate the AAL value as the area under the loss-exceedance probability curve. A synthetic data set of AAL values is generated for buildings located in Special Flood Hazard Area (SFHA – areas with at one-percent or greater chance of experiencing flood in a given year). Using the data set, generalized equations are developed to estimate AAL of buildings that are located in the SFHA using flood hazard parameter. Then, a machine learning model is trained on a synthetic data set of AAL values that considers flood hazard parameters, building replacement values, and insurance parameters (i.e., coverage and deductible) to apportion the flood loss between homeowner and insurer. The model predicts the apportionment factor that is used in determining the homeowner AAL. Finally, flood hazard mitigation in coastal areas by the reduction of wave and surge is quantified as the reduction of base flood elevation (BFE), and subsequently the reduction in homeowner’s AAL is estimated. The result of this research will present generalized AAL equations to estimate flood AAL at individual building level, a machine learning model to predict the apportionment factor to determine the homeowner’s AAL, and the reduction of AAL for different hazard mitigation designs (wave and surge reductions scenarios). This comprehensive methodology will help community officials and government agencies to gain more insights

The increase in urban population and the continuous pressure for cities’ expansion along with the increase in urban flooding phenomena in Greece and worldwide, stress the need for enhancement of flood risk mitigation efforts. West Athens urban area, in Greece, experienced a significant population clustering since the 1950s leading, in some occasions, to a poorly-planned development, even in areas with imminent flood risk. An issue becomes apparent, taking into account the rich flooding record, the extended damages in property and infrastructure and the 76 flood victims during the last century in the area. In this work, flood hazard is assessed in 10 municipalities of West Athens, with the application of a GIS-based methodology that exploits catchment morphometric characteristics to delineate flood hazard zones. Historical flood events are reconstructed to provide better understanding of the flooding problem in the area. Finally flood hazard was studied in conjunction with vulnerability to estimate flood risk spatial distribution. The results showed that areas around Fleva and Eschatia torrent, especially Mpournazi, parts of Ilion and Kamatero and some parts of Peristeri presented the highest flood hazard and risk values. Additionally, moderate flood risk appeared in several mountain torrents in west parts of Petroupoli and Peristeri.