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.

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country’s northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world.

Desertification remains a critical global issue driven by climate change, unsustainable land use, and socio-economic pressures. This bibliometric review provides an in-depth analysis of desertification research from 1974 to 2024 using the Scopus database to identify trends, key players, and methodological advances. Publications grew exponentially during this period, from 21 in 1974 to a peak of 186 in 2023, demonstrating growing academic and policy attention. The analysis found that 4178 authors contributed to 2004 peer-reviewed articles, with China emerging as a leading research hub, contributing 386 publications and leading efforts in environmental restoration projects such as the Great Green Wall. Advanced methodologies, including remote sensing and geographic information systems (GIS), have facilitated large-scale monitoring, despite challenges such as data inconsistencies and limited resolution. Institutions such as Guizhou Normal University and Lanzhou University have led the global research effort, publishing 316 and 124 publications, respectively. Influential journals, including Land Degradation and Development and the Journal of Arid Environments, have played a key role in shaping the discourse. Historical analysis has highlighted the persistent threat of desertification to human societies, exemplified by the decline of civilizations such as the Sumerian and Khorezmian. Despite significant progress, regional differences in research attention persist, with Central Asia receiving limited attention despite its vulnerability. This review highlights the need for standardized methodologies, interdisciplinary approaches, and enhanced international collaboration. By leveraging advanced technologies and sustainable land management practices, the global community can mitigate the environmental and socio-economic impacts of desertification, promoting the resilience of ecosystems and communities while moving toward land degradation neutrality.

Wind energy has emerged as a viable alternative to fossil fuels due to its clean and cost-effective nature. Pakistan, facing growing energy demands and the imperative to reduce carbon emissions, has invested significantly in wind power to supply electric power in rural and urban communities, particularly in the Thatta district of Sindh Province of Pakistan. However, the sustainability of wind energy generation is contingent upon consistent and sufficient wind resources. This study examines the wind potential of Thatta district from 2004 to 2023 to assess its suitability for large-scale wind power development. To evaluate the wind potential of Thatta district, seasonal wind speed and direction data were collected and analyzed. Wind shear at different heights was determined using the power law, and wind potential maps were generated using GIS interpolation techniques. Betz’s law was employed to assess wind turbine power density. Box–Jenkins ARIMA and SARIMA models were applied to predict future wind patterns. This study revealed that Thatta district experienced sufficient wind speeds during the study period, with averages of 9.7 m/s, 7.6 m/s, 7.4 m/s, and 4.8 m/s for summer, autumn, spring, and winter, respectively. However, a concerning trend of decreasing wind speeds has been observed since 2009. The most significant reductions occurred in summer, coinciding with Pakistan’s peak electricity demand. While Thatta district has historically demonstrated potential for wind energy, the declining wind speeds pose a challenge to the sustainability of wind power projects. Further research is necessary to identify the causes of this trend and to explore mitigation strategies.

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup.