This paper has attempted to determine the weighting levels of the soil and ground motion parameters (engineering bedrock depth (EBd), average shear wave velocity (Vs30), fundamental frequency (f0), peak ground acceleration (PGA), Joyner-Boore distance (Rjb), and epicenter distance (Repi)) in reflecting the actual damage status after the 2023 Kahramanmara & scedil; earthquakes, which have a wide impact area of 11 provinces. The analytical hierarchy method (AHP), a multi-criteria decision-making (MCDM) process, was used to analyze these parameter data sets obtained from 44 Disaster and Emergency Management Presidency of T & uuml;rkiye (AFAD) stations (Gaziantep, Hatay, Kahramanmara & scedil;, and Osmaniye). The priority order of the parameters before the analysis was systematically collected. These parameters were categorized into soil, ground motion and earthquake source-path properties. Considering the literature, these characteristics and their combined effects were systematically weighted with AHP under five groups. According to the weighted groups in the scope of the study, the actual damage data can be determined with a minimum accuracy rate of 70% (Group 1). In comparison, the best performance evaluation was 82% (Group 5). The parameter order and weights in the actual damage data evaluation are suggested as EBd-%28, PGA-%24, Vs30-%19, Rjb-%14, f0-%10, and Repi-%5 considering the very high accuracy rate of Group 5. This suggested weighting allows the rapid and effective estimation of the damage distribution after a possible earthquake only with soil, ground motion and earthquake source-path characteristics, even in cases where reliable structure data cannot be obtained.

Wildfires lead to socio-economic and environmental impacts. These impacts include hydrological instability, which can cause severe damage, especially where infrastructures are present. Post-rehabilitation measures can be useful in reducing or preventing erosion or hydrogeological risks. Decision-makers are called on to prioritize post-fire intervention areas and allocate public funds for this purpose. This work focuses on the assessment of erosion and hydrological risk potential in forested slope areas affected by wildfire using a Multi-Criteria Decision Analysis (MCDA) approach integrated with a GIS environment on a regional scale. Expert perception was considered using the pairwise comparison method as part of the Analytical Hierarchy Process (AHP). This allows expert stakeholders to rank relevant criteria, providing a quantitative metric (weight) for qualitative data. Two MCDA methods are used and compared: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). Fire frequency, slope (gradient and length), and proximity to infrastructures were found to be the most important factors by the stakeholders. The WLC method provides evidence classified into high and moderate suitability class areas characterized by high values for fire frequency or slope gradient. Conversely, the OWA method, ranging from low to high risks, makes it possible to adapt the method and obtain a range of suitability maps. Novelties of the MCDA-GIS combined methodology adopted in this work are its application on a regional scale and the combination of vulnerability and driving-force factors (namely presence of grey infrastructures, fire frequency). The MCDA-GIS methodology can be suitable for public administrations in that it allows for mapping a regional area more quickly and thus facilitates sector planning.

Landslides are significant geological hazards in mountainous regions, arising from both natural forces and human actions, presenting serious environmental challenges through their extensive damage to properties and infrastructure, often leading to casualties and alterations to the landscape. This study employed GIS-based techniques to evaluate and map the landslide susceptibility in the Bekhair structure located within the Zagros mountains of Kurdistan, northern Iraq. An inventory map containing 282 landslide occurrences was compiled through intensive field investigations, as well as the interpretation of remote sensing data and Google Earth images. Ten potential influencing factors, including elevation, rainfall, lithology, slope, curvature, aspect, LULC, NDVI, distance to roads and rivers, were selected to construct susceptibility maps by integrating the frequency ratio (FR) and analytical hierarchy process (AHP) approaches, with the goal of understanding how these factors relate to landslides occurrence. The Bekhair core area was divided into 5 hazard zones on the landslide susceptibility maps. The regions classified as very low and low hazard zones are mainly occur in flat or gently sloping plains that characterized by resistant rocks, dense vegetation, minimal rainfall, shallow valleys, and are distant from riverbanks and roads. The areas designated as high and very high hazard zones are found in steep slopes and rough terrain with bare soil, intense weathering, high rainfall, sparse vegetation, highly fractured rocks, deep valleys, and close proximity to construction projects. The moderate hazard zones are mainly located between the other 4 zones across the Bekhair anticline. Results of the susceptibility analysis indicate that the occurrence of landslides in Kurdistan mountains are primarily controlled by factors related to the tectonic structure, surface characteristics and environmental conditions, such as rock lithology (competency), terrain slope, rainfall intensity, and human impacts. The delineation of landslide hazard zones offers important guides for government decision-makers engaged in regional planning, infrastructure development, and the formulation of strategies to mitigate landslides and protect lives and properties in Kurdistan. The accuracy of susceptibility maps was evaluated using the R-index and the AUC-ROC curve. The landslide susceptibility index (LSI) values allocated to different susceptibility classes derived from both FR and AHP models are consistent with the values obtained from the R-index. Moreover, the FR model demonstrated superior performance compared to the AHP model, with a success rate of 85.3% and a predictive rate of 81.2%, in contrast to the AHP model's success rate of 75.2% and predictive rate of 72.4%.

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

The eastern of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (rho l), transverse resistance (TR), coefficient of electrical anisotropy (lambda), resistivity of formation (rho m), fracture porosity (phi f), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Na & iuml;ve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15-2253 m(2)/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

Risk assessment is vital for humanities, especially in assessing natural and manmade hazards. Romblon, an archipelagic province in the Philippines, faces frequent typhoons and heavy rainfall, resulting in floods, with the Municipality of Santa Fe being particularly vulnerable to its severe damage. Thus, this research study intends to evaluate the flood risk of Santa Fe spatially using the fuzzy analytical hierarchy process (FAHP), taking into account data sourced from various government agencies and online databases. GIS was utilized to map flood-prone areas in the municipality. Hazard assessment factors included average annual rainfall, elevation, slope, soil type, and flood height. Distance to river, distance to road, types of building structure, mean age, gender ratio, and average annual income were considered parameters of vulnerability assessment. Exposure assessment considered land use, distance to evacuation facility, household number, and population density. Weights for each parameter were determined through pairwise comparison performed by experts. These weights were then incorporated into risk assessment estimation. The developed risk map identifies five high-risk barangays (small local government units). The study's findings will enable local government units to establish flood mitigation programs, implement targeted mitigation measures, and formulate strategic response plans to lower risk and safeguard the residents of Santa Fe effectively.

Landslides account for the breakdown of natural topographies, impacting many mountainous areas and leading to loss of lives and damaged infrastructure. This research aims to generate a reliable landslide susceptibility zonation map employing geospatial and Analytical Hierarchy Processes (AHP) in Addi Arkay Woreda, North Gondar Zone, Amhara Regional State, northern Ethiopia. The present study uses remote sensing data, geographic information system (GIS) tools, AHP, and weighted linear combination (WLC) models to analyze multiple environmental variables, including slope, aspect, curvature, lithology, soil texture, topographic wetness index (TWI), and rainfall. As per the results, around 186.12 km(2) (13.26%) of the total study area is under very high landslide susceptibility and 140.85 km(2) (10.05%) under very low susceptibility. Using Google Earth images for inaccessible areas, 121 landslide inventories were identified through fieldwork. Of these inventories, 85 were used to train the model and 36 for testing. The performance of the AHP model was validated by the Receiver Operating Characteristics (ROC) curve (0.75), which indicates good predictive accuracy for identifying landslideprone areas. These findings are essential to regional land use planning, hazard mitigation, and landslide prevention efforts. Additionally, this study contributes to the scientific understanding of landslide dynamics in the Northwestern highlands of Ethiopia and offers a methodological framework that can be applied to other regions with similar geological and climatic conditions.

为深入探讨在真三轴应力路径及多因素交互作用条件下冻结钙质黏土的能量特征及其影响程度，本研究借助ZSZ-2000型真三轴冻土试验平台，采用正交试验的设计方法，开展冻结钙质黏土真三轴压缩试验研究。研究结果揭示：在初始压密阶段及线弹性阶段，冻结钙质黏土的输入能相对较低，增长速率缓慢；随着加载过程的深入，输入能逐步增加，其增长速率亦随之提高；至破坏阶段，输入能-等效应力曲线呈现出明显的跃升特征，此时输入能的急剧上升，说明输入能曲线斜率的变化是冻结钙质黏土内部裂纹非稳定发展的微观表现。同时，输入能曲线的突然急升亦指示了失稳破坏的发生。进一步通过极差分析及AHP层次分析可得一致的输入能影响因素优劣次序：含水率w、中主应力系数b、温度T、含盐量φ、围压σ3。含水率对输入能的负向影响显著，温度和中主应力系数b对输入能有着显著的正向影响关系，随着中主应力系数b值的增加，其对冻结试样输入能的影响程度呈减弱的趋势且存在一个最优中主应力系数b=0.33使试样输入能增幅达到最大。

为深入探讨在真三轴应力路径及多因素交互作用条件下冻结钙质黏土的能量特征及其影响程度，本研究借助ZSZ-2000型真三轴冻土试验平台，采用正交试验的设计方法，开展冻结钙质黏土真三轴压缩试验研究。研究结果揭示：在初始压密阶段及线弹性阶段，冻结钙质黏土的输入能相对较低，增长速率缓慢；随着加载过程的深入，输入能逐步增加，其增长速率亦随之提高；至破坏阶段，输入能-等效应力曲线呈现出明显的跃升特征，此时输入能的急剧上升，说明输入能曲线斜率的变化是冻结钙质黏土内部裂纹非稳定发展的微观表现。同时，输入能曲线的突然急升亦指示了失稳破坏的发生。进一步通过极差分析及AHP层次分析可得一致的输入能影响因素优劣次序：含水率w、中主应力系数b、温度T、含盐量φ、围压σ3。含水率对输入能的负向影响显著，温度和中主应力系数b对输入能有着显著的正向影响关系，随着中主应力系数b值的增加，其对冻结试样输入能的影响程度呈减弱的趋势且存在一个最优中主应力系数b=0.33使试样输入能增幅达到最大。

为深入探讨在真三轴应力路径及多因素交互作用条件下冻结钙质黏土的能量特征及其影响程度，本研究借助ZSZ-2000型真三轴冻土试验平台，采用正交试验的设计方法，开展冻结钙质黏土真三轴压缩试验研究。研究结果揭示：在初始压密阶段及线弹性阶段，冻结钙质黏土的输入能相对较低，增长速率缓慢；随着加载过程的深入，输入能逐步增加，其增长速率亦随之提高；至破坏阶段，输入能-等效应力曲线呈现出明显的跃升特征，此时输入能的急剧上升，说明输入能曲线斜率的变化是冻结钙质黏土内部裂纹非稳定发展的微观表现。同时，输入能曲线的突然急升亦指示了失稳破坏的发生。进一步通过极差分析及AHP层次分析可得一致的输入能影响因素优劣次序：含水率w、中主应力系数b、温度T、含盐量φ、围压σ3。含水率对输入能的负向影响显著，温度和中主应力系数b对输入能有着显著的正向影响关系，随着中主应力系数b值的增加，其对冻结试样输入能的影响程度呈减弱的趋势且存在一个最优中主应力系数b=0.33使试样输入能增幅达到最大。