Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.

Aeromagnetic data from the Ogoja region, Southeast Nigeria, were enhanced using high-precision methods including the tilt angle of total horizontal gradient (TAHG), the softsign function (SF), and the improved logistic function (IL) with the aim of creating a new structural map. This new map can help improve the understanding of the trend, spatial distribution, and pattern of the lineaments. The TAHG, SF, and IL methods generated geologic structures with correlating trends, distributions, and patterns. However, the SF and IL techniques mapped the borders of geologic structures more precisely. The lineaments extracted from the SF and IL maps were reduced to equator (RTE) magnetic data, and a GIS was used to create structural maps with NE–SW, NW–SE, NNE–SSW, and NNW–SSE orientations. Furthermore, the depths (0–2100 m) of these geologic structures were estimated using the tilt depth technique (TDT). The high lineament density and thin sedimentation observed in the study area were triggered by the widespread Santonian igneous intrusions associated with the Abakaliki Anticlinorium. The techniques applied in our study can be employed in areas with the same conditions around the world for the precise delineation of geologic structures from magnetic and gravity data.

Coastal environments need continuous environmental risk assessment, especially with increasing coastal development and human activities. The present work evaluates the distribution, contamination, and environmental risk of potentially toxic elements (PTEs) in coastal sediments between Al-Jubail and Al-Khafji cities along the Arabian Gulf, Saudi Arabia, and documents the influence of background references applied in pollution indices. Thirty-two sediment samples were collected for analysis of Ni, Cu, Cr, As, Zn, Pb and Hg using ICP-AES. The ranges of PTEs (mg/kg) were in the following order: Cr (3.00–20.0), Ni (2.00–32.0), Zn (2.00–14.0), As (2.00–4.00), Pb (1.50–5.00), Cu (1.00–5.00), and Hg (0.50–1.00). The coastal sediments show severe enrichment with As and Hg, and no to minor enrichment and a low contamination with Cr, Cu, Cr, Zn, and Pb. Based on sediment quality guidelines, concentrations of Cu, Pb, Zn, As, and Cr do not represent a concern for benthic communities, while Ni and Hg show a risk for benthic communities in four and 17 sampled areas, respectively. Multivariate analysis indicated a geogenic source for Zn, Cr, Cu, Ni, and Pb, mixed natural and anthropogenic sources for As, and an anthropogenic source for Hg, mostly from oil pollution, sewage, and industrial effluents spreading near Al-Jubail industrial city.

This study presents a novel multi-model fusion approach for enhancing land surface temperature (LST) recovery and spatial resolution improvement. We compare and integrate linear Ridge regression and non-linear Random Forest (RF) ensemble models to retrieve high-resolution LST from thermal infrared (TIR) remote sensing data. Initially, LST is computed using a single-channel (SC) algorithm at 30m (SCLST-30m) and 90m (SCLST-90m). The SCLST-90m is then enhanced through a multi-model fusion framework that combines both ensemble models with high-resolution predictor features, including surface reflectance, multiple indices (SAVI, NDVI, NDWI, NDBI, and UI), and Digital Elevation Model (DEM). The fusion approach integrates the complementary strengths of both models, with RF capturing non-linear relationships and Ridge regression providing stability in linear patterns. Cross-validation between SCLST-30m and the fused LST demonstrates superior performance, with the integrated approach achieving higher accuracy than individual models. Validation against in-situ LST confirms the effectiveness of the fusion framework, yielding R² values of 0.89 and 0.84 for RF and Ridge models respectively. The RMSE values for SCLST-30m, RF-DLST, and Ridge-DLST are 0.37 K, 0.38 K, and 0.45 K, respectively. These results demonstrate the effectiveness of our multi-model fusion approach in achieving accurate recovery and resolution enhancement of LST, offering valuable insights for environmental monitoring and urban planning applications.

Extreme climatic events, such as floods, are becoming increasingly frequent and severe worldwide, including in Pakistan. The Swat River Catchment (SRC), located in the eastern Hindukush region of Pakistan, is highly susceptible to flooding due to its unique geographical and climatic conditions. However, despite the region’s susceptibility, comprehensive flood risk assessments that integrate hazard, vulnerability, and exposure components remain limited. To address this gap, this study assesses flood risk in the SRC using 22 indicators distributed across the three core dimensions of flood risk: hazard, vulnerability, and exposure. Flood hazard was modeled using 11 indicators, broadly categorized into environmental, hydrological, and geographical aspects, while vulnerability was evaluated through socio-economic factors, geographical proximity, and land use characteristics. Exposure was analyzed based on population metrics and critical infrastructure. All data were converted into thematic layers in GIS, systematically weighted using the Analytical Hierarchy Process (AHP) and combined to produce hazard, vulnerability, and exposure maps respectively. These maps were then integrated through a risk equation to generate the final flood risk map. The results reveal that 31% of the study area is in a high flood risk zone, 27% in moderate risk zones, 23% in low risk, and 19% are safe areas. The results were validated using the Area Under the Curve (AUC) technique, yielding a value of 0.92, which indicates high reliability. By presenting the first integrated flood risk assessment for the SRC, this study provides valuable insights into flood-prone areas and risk distribution. These results highlight the urgent need for enhanced flood risk management, especially in urban areas. The developed methodology serves as a valuable tool for disaster management authorities and planners, helping them make risk-informed decisions, allocate resources efficiently, and implement targeted flood mitigation strategies.

Soil contamination is a major issue that endangers the ecology in most countries. Total concentrations of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, VFe, and Zn were determined by analyzing soil samples from 32 surface soil samples in southwest Saudi Arabia, including certain areas of Al-Baha. Kriging techniques were used to create maps of the distribution of metal. To assess the levels of soil contamination in the research area, principal component analysis (PCA), contamination factors (CF), and pollution load index were used. The results show the stable model gave the best fit to the As and Zn semivariograms. The circular model fits the Cd, Co, and Ni semivariograms the best, while the exponential model fits the Cr, V, and Fe semivariograms the best. For Ni and Pb, respectively, spherical and Gaussian models are fitted. The findings demonstrated two clusters containing different soil heavy metal concentrations. According to the data, there were two different pollution levels in the research region: 36.58% of it is strongly contaminated, while 63.41% of it has a moderate level of contamination (with average levels of these metals 5.28 ± 5.83, 0.81 ± 0.19, 18.65 ± 6.22, 45.15 ± 23.25, 60.55 ± 23.74, 972.30 ± 223.50, 33.45 ± 14.11, 10.05 ± 5.13, 84.15 ± 30.72, 97.40 ± 30.05, and 43,245.00 ± 8942.95 mg kg⁻¹ for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, Fe, and Zn, respectively). The research area’s poor management practices are reflected in the current results, which raised the concentration of harmful elements in the soil’s surface layers. Ultimately, the outcomes of pollution concentration and spatial distribution maps could aid in informing decision-makers when creating suitable heavy metal mitigation strategies.