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Abstract Introduction The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas.

The study investigates the existing scenario and spatial distribution pattern disparity of service distribution of water, sanitation, and hygiene (WASH) and waste management services (WMS) in urban informal settlements within the Zaria metropolis and generates recommendations for the policymakers for improvements. The study highlights challenges in urban settlements, including insufficient water, sanitation, and waste services, contributing to diseases like diarrhea and cholera. It stresses the importance of better water quality, sanitation, and waste management, and highlights the impact of governance and policies on service gaps. It also suggests using collaborative methods and technologies like GIS for solutions, employing GIS and statistics to analyze service availability and access for residents. The findings underscore the dominant role of the private sector in water treatment plant distribution (71.43%) over public facilities (28.57%). It highlights disparities in toilet types between public and private spaces: pit toilets (55.11%) are prevalent in public restrooms, while the data indicate that most water sources are poorly maintained: Only 0.50% of boreholes are newly constructed, while a significant 43.15% are poorly maintained, and 30.62% are well maintained. Moreover, private toilets are predominantly pit toilets, constituting 3.11% of the total, with no water closets toilets reported. HIGHLIGHTS The study conducted a comprehensive analysis of water, sanitation, and waste management services in Zaria metropolis, Kaduna, Nigeria.; The research highlighted the urgent need for improved maintenance practices of water sources.; The study identified significant challenges in sanitation facilities, with only 34% of the population having access to improved sanitation options.;

Water is necessary for all biological life and industrial, municipal, agricultural, and residential processes. It is challenging to imagine living without water. Unfortunately, human and natural activities are causing the sources of useable water to become contaminated. Despite having enormous and unique natural water resources, Africa has experienced unprecedented environmental pollution because of the abuse of these resources. Additionally, population increase and urbanization brought by technological advancements have significantly worsened water pollution in Africa. The significant causes of pollution for surface waterways are untreated effluents released into the environment by humans and machines. It is still being determined if the emission goals set by several African countries for surface water discharge have been fulfilled. Wells and boreholes are essential sources of drinking water for Africans. However, because of their location in sterile areas, the natural water quality of these groundwater sources could be better. The primary sources of water pollution in Africa include agricultural activities, mining, roadside discharges, trash from companies and workshops, landfills, and e-waste. Oil leaks are a severe problem in oil-rich African countries. Lake Tanganyika is East Africa's most significant freshwater reservoir, while Lake Victoria is the second-deepest lake in the world. HIGHLIGHTS Evaluate scientific findings on water quality in the lakes on the African continent.; Identify the principal contaminants of surface water.; Pearson Correlation is used to identify the relationship among the water parameters.;

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

A flood is a natural disaster. Heavy rainfall and overflow frequently cause enclosed land areas to fill with water, resulting in considerable loss of human life and property, including damage to buildings, bridges, electric supply networks, and transportation, and economic concern. This work was carried out in the Upper Indus Basin (UIB). We developed an artificial intelligence model for forecasting the flood events in this study. Long-short term memory (LSTM) and seasonal auto-regressive integrated moving average (SARIMA) were used in this study to forecast flood events. This study used a dataset from 1971–2009 and divided it into training, testing, and forecasting from 1971–2004, 2005–2009, and 2010–2014, respectively. The best statistical analysis result was observed with the LSTM model, which documented the value of root mean squared error (RMSE) at 22.79 and 35.05 for training and testing, respectively. Hence, the results of the study highlight that the LSTM model was the most suitable among the artificial neural networks for flood event forecasts. This current study will help in the forecasting of high storms for effective water resources management.

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents.