Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R²_adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R² = 0.99), and the SVM model was the lowest (R² = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.

Invasive alien species pose a serious threat to the world’s ecosystems and livelihoods. This review aimed to investigate the negative and positive impacts of Prosopis juliflora (Sw.) DC on ecosystems in Kenya and Ethiopia. The bibliographic analysis highlighted that Prosopis juliflora was introduced to Kenya and Ethiopia to tackle desertification, ensure the self-sufficiency of wood products, and conserve water and soil in semi-arid areas. Years later, this species has turned invasive; although the communities in infested areas have realized some benefits from this tree such as obtaining timber, charcoal, firewood, shelter, and livestock feed from its pods; the negative impacts of this tree have far outweighed the positives. Notably, this invasive plant has significantly impacted agricultural and livestock productivity by invading pasturelands and farmlands. For instance, the negative health implications for livestock significantly reduce their productivity. Similarly, this plant has reduced the diversity of the native trees, herbaceous, and grasses because of its ability to outcompete them. Control methods such as biological, mechanical, chemical, and control by utilization have been tried but none have so far been effective in controlling this invasive plant. Our results showed that Prosopis juliflora is a serious invader that has a huge potential to invade the arid and semi-arid lands of Kenya and Ethiopia. Therefore, stakeholders at all levels need to implement integrated management strategies to reduce the spread and the negative impacts of this species.

Understanding the interplay between climate change and land use/land cover (LULC) dynamics is critical for sustainable resource management, particularly in vulnerable Mediterranean ecosystems. This study introduces a novel framework integrating the Soil and Water Assessment Tool (SWAT) with future LULC and climate scenarios to predict hydrology and sediment dynamics in the 9th April watershed, a data-scarce river basin catchment in northern Morocco. By exploring four temporal scenarios up to 2050, the study comprehensively analyses erosion and water yield dynamics under changing environmental conditions. The framework employs robust calibration and validation using five statistical metrics, including Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), with NSE as the objective function. Sensitivity analysis identifies critical parameters such as bulk density (SOL_BD), curve number (CN2), and soil evaporation compensation factor (ESCO), which govern water retention and runoff. For sediment transport, parameters like soil erodibility (USLE_K) and supporting practice factor (USLE_P) significantly influence deposition rates, projected to decrease from 49.6 t/ha/year to 16 t/ha/year under Scenario 4. This research demonstrates the novelty of integrating climate and LULC projections in a single modeling framework for Mediterranean drainage basins, offering insights into adaptive water management and erosion control strategies. The results highlight key vulnerabilities and adaptive opportunities, emphasizing the global applicability of this approach for data-scarce regions experiencing similar challenges. This study provides a predictive tool to address gaps in understanding hydrological and sediment dynamics under future climate and land-use change, providing practical insights for policymakers and resource managers. By identifying the key drivers of erosion and water yield, the results will support targeted interventions to enhance climate resilience and the sustainable management of land and water resources. The adaptability of the framework highlights its potential as a benchmark for addressing environmental challenges in climate-sensitive ecosystems worldwide. Future research should refine these models by incorporating socio-economic factors and stakeholder engagement to ensure practical application in decision-making.

Coagulation and flocculation remain foundational in drinking water treatment plants (DWTPs) worldwide. Determining optimal coagulant dosage remains a persistent challenge due to raw water variability and reliance on conventional jar tests, labor-intensive, time-consuming methods, and being prone to operational inefficiencies. This study introduces a novel machine learning (ML) based framework leveraging tree-based ensemble models to predict coagulant dosing with high precision, offering a transformative alternative to empirical approaches. Seven algorithms, Random Forest (RF), ExtraTree, REPTree, and M5P (M5 Prime) Tree, RF-Extra Tree, RF-REPTree, RF-M5P Tree, were systematically evaluated using real-world operational data, incorporating five key water quality parameters: turbidity (Tb), Electric conductivity (EC), pH, temperature, and dissolved oxygen (DO). The performance of the proposed models was assessed based on Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Willmott's index of agreement (WI), and the coefficient of determination (R²) values. Among the models, the RF-Extra Tree model exhibited superior predictability (RMSE = 0.515, MAE = 0.329, NSE = 0.9850, WI = 0.996, KGE = 0.969 and R² = 0.985), significantly outperforming traditional heuristics and other models. The RF algorithm also demonstrated robust results (RMSE = 0.807, MAE = 0.586, NSE = 0.963, WI = 0.99, KGE = 0.899, and R² = 0.963), highlighting its potential for deployment in dynamic, data-driven treatment environments. This research not only underscores the capacity of ensemble learning to model the complex, non-linear relationships inherent in water treatment but also provides a scalable decision-support tool capable of enhancing treatment consistency, reducing chemical use, and optimizing operational efficiency across diverse geographic and climatic conditions. The proposed methodology holds global relevance for advancing smart water treatment infrastructure, offering both environmental and economic benefits to utilities and stakeholders.

A wetland is an area of land that is covered by water, whether salt, fresh, or somewhere in between, either seasonally or permanently. It operates as a separate ecosystem. Wetlands are distinguished from other types of land or bodies of water primarily by vegetation that has adapted to moist soil. However, this study focused on identifying wetlands, and change in Addis Ababa, the study employs remote sensing and GIS software. USSG provides free pictures from Landsat 5 TM and Landsat 8 OLI. The area is separated into six classes, with a Confusion Matrix reaching an accuracy of 95.58% for images taken between 1986 and 2019, and 86% in 2019. The Kappa Coefficients (k) are 0.8645 and 0.82, respectively. The Confusion Matrix also reports the Kappa Coefficient (k), which is another indicator of categorization accuracy. The k values for the picture classifications in 1986 and 2019 were 0.8645 and 0.82, respectively. Wetlands totaled 52.66 km2 of land in the original condition (1986) image; in 2019, that figure had reduced to 17.03 km2. The distribution of land cover types in 1986 and 2019 for built-up and other concerns influenced the demise of the marsh in and around the city. To develop a smart city and a green, climate-resilient economy, immediate and mid-term restoration management plans are required.

The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation, and carbon sequestration. Conventional direct BD measurement methods are often time-consuming, costly, and laborious. In contrast, the use of pedotransfer functions (PTFs) based on readily available soil properties offers a cost-effective alternative approach that can provide indirect quick estimates of soil BD. Although several region-specific PTFs are available, none is tailored for use in the hilly North-Western Himalayas (NWH) area. Thus, this work developed a novel PTF to estimate the soil BD of 300 soil samples collected from the NWH area and analyzed by stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT). The site-specific SR-based equation performed better than the PLSR model by explaining up to 72.2% variability of BD and achieving a coefficient of determination (R²) of 0.73, a mean absolute error (MAE) of 0.0379 g cm⁻³, and a root mean square error (RMSE) of 0.0487 g cm⁻³ for the BD prediction. Soil organic matter and clay contents were the most significant contributors, showing the highest importance in the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation (R²: 0.006–0.71, RMSE: 0.0649–0.405 gcm^− 3, MAE: 0.0512–0.395 g cm^− 3). The promising SR equation and PTF developed in this work showed high accuracy and excellent suitability for stakeholders and professionals in the study area and the scientific community as a reliable BD estimator for hilly soils. This study introduces proposed the first-ever site-specific PTF for BD estimation in hilly soils, contributing to scientific challenge by enhancing soil database and improving BD predictions in complex terrains worldwide. In terms of global significance, the outcomes of this study are essential for soil quality and vulnerability studies, resilience indicators estimates carbon sequestration initiatives, and digital soil mapping, and can also be used to construct a BD database for hilly soils across the globe. Graphical Abstract The evaluation of soil bulk density (BD) is essential for studying soil processes like water retention, air circulation and carbon sequestration, and soil quality, vulnerability and resilience. Conventional direct methods to measure soil BD are often time-consuming, costly and laborious, whereas pedotransfer functions (PTFs) based on readily available soil properties offer an efficient alternative approach that provides indirect quick estimates of soil BD. However, none of the several region-specific PTFs currently available, is tailored for use in the hilly North-Western Himalayas (NWH) area. A novel PTF was developed in this work to evaluate the BD of 300 soil samples collected from agriculture, horticulture, forest and fallow hilly soils in the North West Himalaya area. Stepwise regression (SR), partial least-squares regression (PLSR), multiple linear regression (MLR), linear regression (LR), and advanced machine-learning algorithms, including artificial neural networks (ANN) and decision tree (DT) were tentatively applied for BD estimation of the studied soils, showing that the site-specific SR-based equation performed the best. Various soil properties were evaluated in the models, with soil organic matter and clay contents being the most significant contributors to the projection. Furthermore, the performance of the SR-based PTF was validated by comparison with 63 PTFs currently in use, which showed to perform poorly and vary widely in BD estimation for the hilly soils studied in this work. In conclusion, the SR equation and PTF developed in this work show excellent and promising suitability for stakeholders and professionals in the study area and for the global scientific community as a reliable BD estimator for hilly soils. Furthermore, the results of this work can be used for soil management, land use planning, and climate change mitigation in fragile ecosystems like the Himalayas.