Freshwater scarcity and rising wastewater generation have intensified global reliance on desalination and reuse, with reverse osmosis (RO) providing 65–70% of installed desalination capacity and achieving energy reductions from 15 kWhm−3 in the 1970s to 1.8–2.5 kWhm−3 today. However, fouling caused by organics, inorganics, microorganisms, and colloids remains the major operational challenge, accounting for ≈25% of RO costs and over USD 15 billion annually. This review synthesises fouling mechanisms and mitigation strategies in renewable energy (RE)-driven RO wastewater-treatment systems, where intermittency exacerbates fouling through start–stop cycles and low-shear conditions. Analysis of recent literature highlights that mixed fouling reduces flux by 10–30%, increases transmembrane pressure, and deteriorates permeate quality. Advances in pretreatment (coagulation, MF/UF), antifouling membranes (hydrophilic coatings, zwitterionic surfaces), and cleaning protocols (osmotic backwashing, nanobubbles) have improved performance, yet complete prevention remains elusive. Persistent gaps include predictive fouling models, standardised performance metrics, and scalable green chemistries for silica and combined fouling control. Future directions emphasise integrated solutions combining advanced materials, AI-driven monitoring, and renewable-aware operational strategies, alongside circular economy approaches for brine valorisation. These innovations are critical for achieving sustainable, low-carbon RO systems for global water security.
Groundwater-dependent irrigated agriculture in semi-arid basins is increasingly threatened by climate variability and aquifer depletion. Karapınar district, located within Türkiye’s Konya Closed Basin, represents a water-stressed melon-producing area where deep-well drip irrigation and rigid calendar-based scheduling remain common. This study developed a prototype AquaCrop-based decision-support framework for adapting mature and pickled melon irrigation to climatic conditions classified by the crop-season weighted three-month Standardized Precipitation Index (SPI-3). SPI-3 was selected to represent short-term seasonal precipitation anomalies relevant to agricultural water availability, and three historical years were used as representative scenarios: Dry (2020), Normal (2018), and Wet (2017). Fourteen irrigation treatments, including farmer practice and phenologically targeted Late-Season Event (L.S.E.) deficit strategies, were simulated using FAO AquaCrop v7.1. Irrigation timing was more important than seasonal irrigation volume alone. Under Normal conditions, an L.S.E. strategy that redistributed irrigation toward four late fruit-development events (T4.2) maintained yield stability comparable to farmer practice (CV < 2%), increased mature melon yield by 12.7% relative to farmer practice, and reduced applied irrigation by 19–21%. Under Dry conditions, a lower-volume L.S.E. strategy with the same late-season targeting (T4.3) maintained baseline-level yield while reducing applied irrigation by 28–31%. The resulting SPI-based decision matrix is forecast-compatible but not operationally validated; field validation, long-term climate testing, and forecast-skill evaluation remain necessary before deployment.
Despite the increasing use of machine learning (ML) and deep learning (DL) for leak detection and localization in water distribution networks (WDNs), standardized datasets, common evaluation protocols, and consistent reporting practices remain limited, thereby restricting meaningful comparison among studies. In this systematic review, PRISMA 2020 was followed to examine the benchmarking practices, data sources, sensing modalities, and evaluation strategies adopted in recent ML/DL-based leak-detection studies. Scopus was searched in April 2026, and 90 studies were included after screening against predefined eligibility criteria. Substantial variation was identified in dataset generation, model validation, and performance assessment. Thirty studies were assigned to the public-benchmark category, but only 23 used one of the three named water-leak resources tracked in this review; the remainder included topology-only reuse or other heterogeneous public resources. Pressure measurements were the dominant sensing modality, whereas flow, transient, and smart-meter data remained comparatively underexplored. Considerable inconsistency was also observed in performance metrics, validation procedures, and leak-scenario definitions, preventing fair comparison of detection and localization performance. Based on these findings, a practical reporting framework is proposed to improve transparency, reproducibility, and comparability. The principal recommendations include standardized dataset documentation, consistent evaluation protocols, expanded community benchmarks, uncertainty quantification, and model interpretability to support operational deployment.
Regional identification and risk mapping of landslide and debris flow hazard chains (LDHCs) are essential for disaster prevention and land-use planning in mountainous regions. This study proposes a machine learning framework for the regional identification and risk mapping of LDHCs in southern Gansu Province, China. A spatial database was established using an inventory of 160 landslide hazard chains (LHCs) and 121 debris-flow hazard chains (DHCs), together with eight environmental conditioning factors. Considering the distinct geomorphological characteristics of the two hazard types, grid-based and watershed-based mapping units were adopted for landslide and debris-flow susceptibility mapping, respectively. Five ensemble learning algorithms were evaluated to identify the optimal susceptibility models. Hazard maps were subsequently generated by integrating susceptibility with earthquake- and extreme rainfall-triggering factors, and regional risk maps were produced by incorporating population and Gross Domestic Product (GDP) exposure data. The Random Forest model achieved the best performance for LHC susceptibility mapping, with an accuracy of 84.9% and an AUC of 0.914, whereas the Extra Trees model performed best for DHC susceptibility mapping, with an accuracy of 92.6% and an AUC of 0.978. The resulting risk maps indicate that the middle and lower reaches of the Bailong River, including Wudu, Zhouqu, Qin’an, and Tianshui, represent the highest-risk areas for LDHCs. The proposed framework provides an effective and transferable approach for regional identification and risk mapping of LDHCs, offering valuable support for disaster prevention, emergency planning, and land-use management in mountainous regions.
Increasing anthropogenic pressures and nutrient inputs threaten the ecological status of coastal ecosystems along the Aegean coast of Türkiye. This study evaluated coastal water quality and trophic conditions using physicochemical parameters, nutrient concentrations, chlorophyll-a, national eutrophication criteria, the Trophic Index (TRIX), and multivariate statistical analyses across 25 coastal stations monitored during 2022–2023. While basic physicochemical variables showed limited spatial variability, nutrient concentrations, chlorophyll-a, and TRIX values exhibited pronounced spatial differences among stations (p < 0.05), identifying Bostanlı (Inner İzmir Bay) as the principal eutrophication hotspot. Principal Component Analysis (PCA) revealed two dominant environmental gradients: a Nutrient Enrichment Gradient associated with total phosphorus (TP), ammonium, nitrite, and nitrate nitrogen (32.6% of total variance), and a Hydrographic–Thermal Gradient associated with salinity, conductivity, total dissolved solids, temperature, and dissolved oxygen (30.4%). Hierarchical and k-means clustering analyses further distinguished environmentally coherent coastal sectors and confirmed the clear separation of Bostanlı from the remaining sampling stations. The close agreement among nutrient distributions, chlorophyll-a concentrations, TRIX values, and multivariate analyses indicates that localized anthropogenic nutrient enrichment, rather than basin-scale hydrographic variability, drives trophic differentiation along the coast. These findings demonstrate the value of integrated monitoring approaches combining physicochemical, trophic, and multivariate indicators for identifying eutrophication hotspots and supporting ecosystem-based management in Mediterranean coastal ecosystems.
