Search Results (1,527)

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms. Full article

Cool roof and green roof have been acknowledged as effective heat mitigation strategies for fighting against the urban heat island (UHI). However, empirical data in hot–humid climate are still insufficient. Experimental conventional, cool and green roofs (three types) were established to comprehensively investigate the thermal performances in Hong Kong under typical summer conditions, as retrofitting strategies for an office building. The holistic vertical thermal behavior was investigated. The comparative cooling potentials were assessed. The results reveal a “vertical thermal sequence” in peak temperatures of each substrate layer for the conventional, cool and green roofs on a sunny day. However, local reversion in the thermal sequence may occur on a rainy day. Green roof-plot C (GR_C) demonstrates the highest thermal damping effect, followed by plot B (GR_B), A (GR_A) and the cool roof (CR) in summer. On a sunny day, the thermal dampening effectiveness of the substrates in the three green roofs is consistent: drainage > soil > water reservoir > root barrier. The holistic vertical thermal profiling was constructed in a high-rise office context in Hong Kong. The diurnal temperature profiles indicate all roof systems could effectively attenuate the temperature fluctuations. The daily maximum surface temperature reduction (SDMR) was introduced for cooling potential characterization of the cool roof and green roofs with multiple vegetation types. On a sunny day, the cool roof and green roofs all showed significant cooling potential. SDMR on the concrete tile of the best performing system was GR_C (26 °C), followed by GR_B (22.4 °C), GR_A (20.7 °C) and CR (13.3 °C), respectively. The SDMR on the ceiling ranked as GR_C, GR_B, GR_A and CR, with 2.9 °C, 2.4 °C, 2.1 °C and 2.1 °C, separately. On a rainy day, the cooling effect was still present but greatly diminished. A critical insight of a “warming effect at the ceiling” of the green roof was revealed. This research offers critical insights for unlocking rooftop cooling potential, endorsing cool roof and green roof as pivotal solutions for sustainable urban environments. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

A two-year (2024–2025) field experiment was conducted in southern Xinjiang to alleviate soil compaction and severe salinization in saline–alkali soils and to evaluate the combined effects of tillage depth and subsurface drain spacing on soil improvement. Six treatments were established with three deep tillage depths, 70 cm (W1), 50 cm (W2), and 30 cm (W3), and two subsurface drain spacings, 20 m (S1) and 40 m (S2). Treatment effects on soil water–salt dynamics, soil physical properties and structure, ionic composition, and subsurface drainage and salt removal were analyzed. This study provides mechanistic and practical evidence that coupling deep tillage with subsurface drainage creates a more effective leaching–drainage pathway than either measure alone and enables robust optimization of design parameters (drain spacing × tillage depth) for saline–alkali land improvement in arid regions. Deep tillage in combination with subsurface drainage significantly increased soil profile water content, total porosity, and cumulative subsurface drainage and salt export, all of which reached their maxima under S1W1; it also significantly reduced bulk density, total salinity, and the concentrations of Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl⁻, and SO₄²⁻, which reached their minima under S1W1. After two spring irrigation–leaching events (in 2024 and 2025), surface salt accumulation in the soil profile was markedly alleviated, and the mean salinity in the 0–20 cm layer decreased by 45.68% across treatments. The S1W1 treatment achieved the best desalinization performance in both leaching events, with reductions of 41.36% and 44.68%, respectively. Pearson correlation analysis indicated that the desalinization effect was significantly negatively correlated with porosity and significantly positively correlated with bulk density and ionic concentrations. Overall, coupling deep tillage with subsurface drainage effectively reduced soil salinity and harmful ions, improved soil structure, and enhanced drainage-mediated salt removal, with the 70 cm tillage depth combined with a 20 cm drain spacing delivering the best performance. Full article

On a planet intending to move toward carbon neutrality while ensuring food security, maximizing water and nutrient use efficiency in agriculture is essential. Soilless cultivation offers a promising solution for food production, yet in substrate-based systems, excess nutrient solution (drainage) is often discarded to maintain phytosanitary safety, resulting in considerable water and nutrient waste. Reusing this drainage requires disinfection to eliminate pathogens. Among available methods, slow sand filtration (SSF) is ecological, economical, and simple, showing strong biological control potential, though not always fully effective against Fusarium oxysporum. Trichoderma atroviride, an antagonistic fungus, may enhance SSF performance. Its antagonistic capacity was evaluated in vitro via direct confrontation assays and in vivo using a closed-loop soilless cucumber cultivation system with eight treatment combinations of SSF, T. atroviride, and F. oxysporum. SSF reduced F. oxysporum incidence by approximately 48%, T. atroviride in irrigation by 44%, and SSF enriched with T. atroviride reached 58% disease incidence reduction, though this increase was not statistically significant. These results confirm that both SSF and T. atroviride can partially suppress F. oxysporum, but further optimization is needed for consistent and complete pathogen control. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

The European Water Framework Directive (2000/60/EC) promotes an integrated approach to water management, recognizing water as a shared resource and defining quality objectives. Within this framework, Sustainable Drainage Systems (SuDS) provide effective solutions to improve water quality, control runoff, mitigate hydrogeological risk, and enhance urban resilience. This study investigates the application of SuDS for quantitative stormwater management in a 290-ha industrial district within the Metropolitan City of Milan. Using a synthetic design storm as a benchmark, the study provides event-scale evidence of the performance of SuDS under observed rainfall events, a topic often underrepresented in the literature. Two hydrologic–hydraulic models were developed using SWMM ver. 5.2: a baseline model representing current conditions and a design model integrating SuDS across 24 hectares. Simulations were performed for four rainfall events representative of typical conditions and for a synthetic 10-year return period design event. Results show that, under observed events, SuDS reduce total CSO volumes by 44% and peak flows by 47%, while decreasing overflow activation by around 11%, with the highest effectiveness during ordinary rainfall conditions. Compared with the synthetic 10-year design event, SuDS exhibit similar volume reductions but lower peak-flow attenuation and overflow frequency reduction, highlighting different system responses under real and design rainfalls. Full article

Some mining sites generate acid mine drainage (AMD)—a highly acidic, metal-rich waste stream that affects bodies of water. Passive treatment systems are widely being adapted, particularly for abandoned or closed mines, due to their cost-effectiveness and lower environmental impact. However, novel strategies and approaches still need to be developed, especially in their implementation. Through batch experiments, this study identifies the effective sequence of three locally available treatment media, namely limestone (LS), steel slag (SS), and activated carbon (AC), using various water quality and pollution indices (WQPIs). The performance of the sequences was assessed based on their ability to improve various in situ parameters (pH, oxidation–reduction potential (ORP), dissolved oxygen (DO), and electrical conductivity (EC)) and their efficiency in removing Fe, Mn, Cu, and SO₄²⁻. Six sequences of media were identified and ranked by calculating a score based on comparisons with the Philippine General Effluent Standard (GES) by normalization and specific WQPIs for AMD and AMD-impacted waters, such as the CCMEWQI, MAMDI, and WPI-AMD. Analysis showed that the sequence of LS-AC-SS and SS-LS-AC yielded the highest removal for heavy metals (98.78% for Fe and Mn and 89.92% for Cu). However, limited removal of SO₄²⁻ was observed (14.96%), which suggests that additional treatment beyond the materials explored must be considered. Considering all the parameters and assessing them through normalization and WQPIs, the sequence of SS-LS-AC achieved the overall best treatment performance. Differences were observed in the ranking between the methods, with WQPIs successfully capturing actual water quality, demonstrating its robustness as an assessment tool. This study shows that the treatment media sequence is a factor in treating AMD, specifically utilizing AC, SS, and LS. Full article

The performance of a building drainage system, “BDS”, is determined by the complexity of internal airflow and pressure dynamics, governed by unsteady wastewater flows from randomly discharging appliances such as WCs, sinks, and baths. Designers attempt to optimise system safety by equalising pressure and incorporating ventilation pipes and active devices such as AAVs and positive pressure reduction devices (PPRDs). However, failures within these systems can lead to foul gases and potentially hazardous microbes entering habitable spaces and posing a risk to public health. This study, for the first time, develops a novel model that simulates the effect of air from the sewer on BDS performance, which describes the correlation between system airflow and air pressure under the influence of air from the sewer. A combination of full-scale laboratory experiments representing a 3-storey building and real-world data from a 32-storey test rig configured as a building demonstrated that sewer air significantly modifies airflow and air pressure within a BDS. These findings are crucial for modern urban environments, where the prevalence of tall buildings amplifies the risks associated with air pressure transients. This work paves the way for updating codes to more effectively address real-world challenges. Full article

This study investigates the spatial and temporal variations in salinity in the Jarahi River and its traditional channels using field measurements and numerical simulations. The primary objective is to assess the effectiveness of different management strategies for salinity reduction under minimum-discharge conditions. Salinity dynamics were analyzed through electrical conductivity (EC) measurements collected over a one-year period and simulated using the MIKE 11 hydrodynamic model. Model performance was evaluated by comparing simulated and observed EC values at key monitoring stations. The results indicate that maximum salinity levels occur during March and April in both the main river and traditional channels, while the highest temporal variability in EC was observed in October. The comparison between observed and simulated data showed a relative error of less than 10%, confirming the reliability of the model simulations. Four management scenarios were evaluated: (1) preventing inflow from the Motbeg drainage, (2) blocking non-centralized drainage inputs, (3) removing all inlet drains, and (4) increasing discharge releases from the Ramshir Dam. The first and third scenarios led to the highest salinity reductions, reaching up to 39% (approximately 1266 µS/cm) in the Gorgor channel, while reductions of up to 53% were observed in traditional streams such as Mansuri and Omal-Sakher under the third scenario. Increasing dam releases resulted in a maximum reduction of 23% (724 µS/cm) at the Gorgor station. Finally, the proposed management strategies significantly reduced salinity levels along the river system, particularly at the entrance of the Jahangiri traditional stream, providing practical insights for salinity control and river basin management. Full article

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system. Full article

Complex water networks face prominent contradictions among flood control, water supply, and ecological protection, and traditional scheduling models struggle to address multi-dimensional water security challenges. To solve this problem, this study proposes a multi-objective coordinated water resources scheduling model for complex water networks, taking the Taihu Lake Basin as a typical case. First, a multi-objective optimization indicator system covering flood control, water supply, and aquatic ecological environment was constructed, including 12 key indicators such as drainage efficiency of key outflow hubs and water supply guarantee rate. Second, a dynamic variable weighting strategy was adopted to convert the multi-objective optimization problem into a single-objective one by adjusting indicator weights according to different scheduling periods. Finally, a combined solving mode integrating a basin water quantity-quality model and a joint scheduling decision model was established, optimized using the particle swarm optimization (PSO) algorithm. Under the 1991-Type 100-Year Return Period Rainfall scenario, three scheduling schemes were designed: a basic scheduling scheme and two enhanced discharge schemes modified by lowering the drainage threshold of the Xinmeng River Project. Simulation and decision results show that the enhanced discharge scheme with the lowest drainage threshold achieves the optimal performance with an objective function value of 98.8. Compared with the basic scheme, it extends the flood season drainage days of the Jiepai Hub from 32 to 43 days, increases the average flood season discharge of the Xinmeng River to the Yangtze River by 9.5%, and reduces the maximum water levels of Wangmuguan, Fangqian, Jintan, and Changzhou (III) stations by 5 cm, 5 cm, 4 cm, and 4 cm, respectively. This model effectively overcomes technical bottlenecks such as conflicting multi-objectives and complex water system structures, providing theoretical and technical support for multi-objective coordinated scheduling of water resources in complex water networks. Full article

The hydraulic performance of woven geotextiles is frequently overlooked in roadway design, despite their extensive use for reinforcement applications. Woven geotextiles are typically manufactured from hydrophobic polymers such as polypropylene or polyester and can act as capillary barriers under unsaturated conditions. This results in moisture accumulation at the soil–geotextile interface, adversely impacting long-term pavement performance. Such problems can be effectively mitigated using geotextiles with enhanced lateral drainage (ELD) capabilities, which are engineered with hydrophilic fibers to facilitate capillary-driven lateral water movement under unsaturated conditions. This functionality facilitates the redistribution of moisture away from the interface, mitigating moisture retention and enhancing drainage performance. The hydraulic performance of geotextiles with enhanced lateral drainage capabilities under unsaturated conditions remains insufficiently understood, particularly in terms of their influence zone and drainage efficiency. For this reason, the present study evaluates the lateral drainage behavior of an ELD geotextile using a soil column test, compared against a control setup without a geotextile and with a non-woven geotextile. Two moisture migration scenarios, namely capillary rise and vertical infiltration, were simulated, with the water table varied at multiple depths. Moisture sensors were embedded along the column depth to monitor real-time water content variations. Results show that the ELD geotextile facilitated efficient lateral drainage, with a consistent influence zone extending up to 2 inches below the fabric. Under infiltration, the ELD geotextile reduced moisture accumulation by 30% around the geotextile, highlighting its superior drainage behavior. These findings encourage practicing engineers to adopt rational, performance-based designs that leverage ELD geotextiles to enhance subgrade drainage and moisture control in pavement and geotechnical applications. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article