Extreme flooding is becoming a more serious hazard to the world's infrastructure, especially in high-risk locations, and is linked to global warming and human activity. This research employs an analytical hierarchy process (AHP) model and geographic information system (GIS) analysis to delineate flood risk zones. An eight-factor multiparametric method to flood risk susceptibility mapping includes precipitation, distance to river, the slope, elevation, land use/cover, topographic wetness index, type of soil, and curvature. An urban flood risk index (UFRI) is established based on vulnerability mapping, revealing that approximately 33% of Haripur District, Khyber Pakhtunkhwa, Pakistan, is prone to floods. Additionally, land use cover analysis indicates that 23% of the crop area in Haripur District is at risk from flood disasters. Recognizing the potential for costly damage to infrastructure, flood hazard mapping serves as a valuable tool to prioritize risk areas for urban and agricultural development. The outcomes of this study are anticipated to significantly contribute to predisaster flood control management in the studied area.

Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion.

The Loess Plateau is marked by intense neotectonic activity and frequent earthquakes. Its unique physico-mechanical properties, combined with the granular overhead pore structure of loess, render it prone to seismic landslides triggered by strong earthquakes. Different types of loess seismic landslides have distinct formation mechanisms, disaster-causing characteristics, and risk assessment programs. In this study, the risk of seismic-collapsed loess landslides as one of the types of loess seismic landslides was evaluated on the Loess Plateau. A risk zoning map for seismic-collapsed loess landslides on the Loess Plateau, considering various exceedance probabilities, was compiled by assessing eight factors. These factors include peak ground acceleration, microstructure of loess, and were evaluated using both the minimum disaster-causing seismic peak ground acceleration zoning method and the analytic hierarchy process. The following conclusions were obtained: (1) Earthquakes are the primary inducing factor for seismic-collapsed loess landslides, with other factors serving as influencers, among which the microstructure of loess carries the highest weight; (2) Across various exceedance probabilities, the likelihood of seismic-collapsed loess landslides occurring at 63% of the 50-year exceedance probability is low. Moreover, as the minimum hazard-causing seismic peak ground acceleration increases, the risk of occurrence of seismic-collapsed loess landslides rises, leading to a gradual expansion of the area share in moderate and high-risk zones; (3) Hazard evaluation results align well with existing data on seismic-collapsed loess landslides and findings from field investigations. The case of seismic-collapsed loess landslides induced by the M6.2 magnitude earthquake in Jishishan County, China, is presented as an illustration. The combined use of the minimum hazard-causing seismic peak ground acceleration zoning method and the analytic hierarchy process method offers a reference for geohazard hazard assessment, with earthquakes as the primary inducing factor and other factors as influencers.

The ecosystem and economy's reliance on clean water is influenced by various factors such as geology, topography, soil types, activities, and the presence of plants and animals. The Ghana Water Company is encountering difficulties in delivering water to consumers in the Ashanti Region due to the shortage of surface water resources, leading to water rationing in the area. Furthermore, poor waste disposal practices, illegal mining, use of fertilizers, and industrial activities have resulted in surface and groundwater source damage. Therefore, there is a need to implement a reliable, simple, and timely method to assess groundwater quality. This study aims to employ GIS and RS techniques to evaluate groundwater quality and potential in the Ashanti Region, Ghana. The Water Quality Index (WQI) was estimated using pH, Total Dissolve Solid (TDS), Chloride, Total Hardness (TH), Nitrate, Temperature, Turbidity, Iron, and Electrical Conductivity (EC). The study then used the WQI distribution to conduct a groundwater potential analysis to identify suitable areas for borehole placement. Digital thematic layers and maps were developed to expose the spatial distribution of water quality parameters, enabling the identification of groundwater pollution control and remedial measures. The study estimated the region's groundwater potential using an integrated GIS and Analytical Hierarchical Process (AHP) technique, grouping under excellent, good, fair, and poor potential. The WQI in the Ashanti Region ranged from 5.208 to 134.232, with 32.252% of the study area having an excellent WQI and 60.168% of the study area having a good WQI. Poor water quality covered 7.550% of the study area. The results showed that the GIS-based AHP approach accurately mapped the spatial distribution of WQI and Groundwater Potential Zones (GWPZ). This information is helpful to planners in water resource management in groundwater exploration and future planning. Policymakers and stakeholders must ensure that groundwater sources are protected from pollution.

The need for sustainable urban growth management and preventive conservation of built elements constitute the key factors in today's increasing demand for the better understanding of subsoil. This information, mainly available from geotechnical surveys, can be integrated into spatial databases to produce operational models. Aiming to generate strategies that enable the visualisation of underground properties in highly anthropised environments, the following four-phase methodology has been proposed: (a) Gathering of geotechnical data; (b) Spatial and statistical analysis; (c) Database design; (d) Generation of 2D and 3D models. Following the aforementioned criteria and using open sources, a spatial dataset of 650 points located within the historical centre of Seville (Spain) has been developed. This urban area is characterised by the heterogeneous distribution of its soil layers and their geotechnical properties. The results show that the application of this method enables a prompt and efficient display of the distribution of geotechnical layers in urban and metropolitan environments, by considering the variations in their mechanical properties. This simplified approach therefore establishes a new starting point for the development of predictive strategies based on approaches of a more complex nature that facilitate the analysis of the interactions between subsoil, buildings, and infrastructures.

Landslide is a natural disaster that often occurs in Indonesia. Landslide is defined as a movement of soil or rock substances, or a mixture of the two substances that move down out of the slopes which is caused by stability disturbances of soil and rock making up the slopes. Losses that can be incurred include loss of life, damage to residences, loss of property, and psychological impacts. In this study, the development of landslide-prone area map was carried out in South Tapanuli Regency based on the estimation model of Pusat Penelitian Tanah dan Agroklimat, Department of Agriculture (2004). The parameters used in this study were rainfall, rock type, slope, land cover, and soil type. Scoring and overlapping method was used to obtain landslide susceptibility levels by using QGIS 3.24 application. According to the study results, there were 4 landslide susceptibility levels in South Tapanuli Regency, namely low landslide susceptibility (142.0093 km(2)), moderate landslide susceptibility (1528.4674 km(2)), high landslide susceptibility (2314.9484 km(2)), and very high landslide susceptibility (379.5909 km(2)). There were 3 districts dominated by moderate landslide susceptibility levels, namely Aek Bilah, Batang Angkola, and Muara Batang Toru districts. Then, there were 10 districts classified in high landslide susceptibility level, namely West Angkola, South Angkola, East Angkola, Angkola Sangkunur, Arse, Marancar, Saipar Dolok Hole, Sayur Matinggi, Sipirok, and Tano Tombang Angkola. Only Batang Toru district classified in very high landslide susceptibility level.

Climate warming has aggravated the occurrence of thaw settlement in permafrost region, but the associated risk has not been precisely assessed or understood. This study applied four machine learning models to explore and compare the spatial distribution of thaw settlement risk in the Wudaoliang-Tuotuohe region, Qinghai-Tibet Plateau, namely, naive Bayesian, k-nearest neighbor, logistic model tree and random forest models. A total of 853 thaw settlement locations and 12 conditioning factors were used to train and validate the above four models. The results indicated that random forest model performed best with the highest accuracy. The risk map produced by random forest model implied that about 76.55% of thaw settlements were located in very high-risk regions, which only occupied 6.85% of study area. The volume ice content, active layer thickness and thawing degree days were the main factors leading thaw settlement. By further comparing the performances between random forest model and other three models, the overestimated and underestimated risk regions (Beiluhe and Tuotuohe basins), and imbalanced conditioning factors (altitude and slope angle) were determined. In contrast with similar studies, this research performed better in model construction and accuracy. The results can help designers to implement precautionary measures in thaw settlement risk management.