Search Results (75)

Climate change is intensifying water scarcity in the Mediterranean region, placing the Antalya province of Türkiye at significant risk due to declining water availability, rapid population growth, and intense tourism activities which increase seasonal demand. This study forecasts population and urban water demand until 2050 and evaluates several water loss reduction scenarios for the city’s drinking water distribution network. In developing the forecasted water demand, the analysis incorporates several water loss reduction scenarios. These include a baseline scenario maintaining current water loss levels, a moderate improvement scenario aligned with Türkiye’s national regulatory targets, and an advanced scenario achieving international best practices. Results show that reducing water losses, caused mainly by aging infrastructure, pressure fluctuations, and leaks, can substantially decrease total water demand. Improved network efficiency is therefore essential for maintaining long-term water security and supporting climate change adaptation efforts in Antalya. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Although South Africa has an extensive water infrastructure, it continues to face significant water scarcity due to its semi-arid climate, increasing urbanisation, ageing infrastructure, and pollution. These challenges, coupled with climate change and increasing water demand, have led to inefficiencies across the water value chain, particularly in rural areas. This review paper evaluates the current adoption of predictive analytics in South Africa’s water management system through a systematic literature review. It identifies the current applications, implementation gaps, and key system components that are suitable candidates to enhance efficiency, resource planning, and long-term sustainability in the sector. The findings show that while predictive models are being applied in urban systems for demand forecasting and proactive maintenance, only 15% of the reviewed studies address their actual adoption in rural or under-resourced contexts. This underscores the need for more inclusive development strategies to ensure equitable water service delivery. Although strides have been made in research and innovation, a major barrier is the slow transition from research to operational deployment, which hinders the full realisation of these technologies’ benefits that are essential for water supply sustainability and availability. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

Urban heat island (UHI) mitigation is essential for improving urban sustainability by reducing heat stress, energy demand, and climate-related health risks. This study evaluates three rooftop measures—highly reflective roofs (HR), green roofs (GR), and rooftop water sprinkling (WR)—in Osaka Prefecture, Japan, using an integrated assessment framework. Temperature changes induced by each measure were simulated using the Weather Research and Forecasting (WRF) model and linked to energy consumption and health impacts through temperature sensitivity coefficients. Health impacts were quantified using disability-adjusted life years (DALYs), and all impacts were monetized for cost–benefit analysis. All measures reduced summer outdoor air temperatures, although their temporal and seasonal effects differed. HR and WR mainly produced daytime cooling, whereas GR provided stronger nighttime cooling. HR and GR increased residential energy consumption due to higher winter heating demand, while WR avoided this penalty through seasonal operation. All measures reduced office and commercial energy consumption and improved health impacts, with GR and WR producing larger benefits than HR. WR achieved the highest benefit–cost ratio, followed by GR and HR. These findings emphasize temporal characteristics, seasonal trade-offs, and spatial targeting in UHI policy. Full article

Accurate forecasting of water demand enables optimized peak-load management, alleviating pressure during high-demand periods and improving the operational efficiency of urban secondary water supply systems—a critical component in the energy-efficient and sustainable operation of buildings. However, existing water demand prediction methods in some regions suffer from low accuracy and excessively long prediction cycles, posing challenges for real-time water scheduling in building-scale systems. To address these challenges, this study develops a hybrid predictive framework that integrates a BP neural network with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms for enhanced parameter optimization. Using hourly water consumption data from a representative residential district, the proposed model is compared against standalone machine learning models—Extreme Learning Machines (ELM), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Model performance is rigorously evaluated using the coefficient of determination, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). The PSO-GWO-BP hybrid model achieves a predictive accuracy of 97.06%, yielding the lowest MAE, MSE, RMSE, and MAPE, as well as the highest R among all models considered, thereby significantly outperforming the benchmark standalone models. Furthermore, the high-precision short-term prediction outputs enable dynamic regulation of secondary water tank refill thresholds, facilitating refined water allocation and enhanced operational management of building water supply systems. These findings demonstrate the considerable application potential of the proposed hybrid model in enhancing both water resource efficiency and energy utilization performance in the daily operation of green buildings, providing reliable technical support for intelligent and low-carbon building water supply management. Full article

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures. Full article

Unraveling the coupling mechanisms of the Water–Energy–Food (WEF) nexus is critical for regional synergistic security and high-quality development. Using an integrated “relationship identification, equation construction, and scenario prediction” framework, this study characterized the spatiotemporal evolution of WEF interactions in the Yangtze River Economic Belt. Under this framework, a Granger causality test coupled with a SHAP interpretability model was first employed to quantify the causal strength among nexus elements, followed by a Bayesian Vector Autoregression model integrated with a hybrid Recurrent Neural Network (RNN) and System Dynamics (SD) approach to simulate evolutionary trajectories from 2024 to 2035. Results showed that: (1) The nexus mechanisms exhibited significant spatial duality. Upstream egg production drove a high virtual water footprint, while inland seafood consumption imposed a non-linear energy premium due to cold-chain dependency. In Shanghai, a strong diesel–groundwater coupling revealed a trade-off between energy input and underground safety. (2) Localized feed cultivation was the core driver for upstream water pressure, whereas logistics intensity was the dominant factor for energy–water interactions in urbanized regions. (3) From 2024 to 2035, the nexus structure will undergo bidirectional divergence. Ecological water demand in the midstream is projected to surge by over 130%, and Anhui’s milk production is forecast to more than double from 107.77 to 225.7 million tons. The findings provide scientific support for coordinating ecological conservation and high-quality development. Full article

In the 21st century, Urbanization, population growth, and climate change have created significant problems in water resource management. Recent advancements in technologies such as Internet of Things (IoT), Edge Computing (EC), Artificial Intelligence (AI), and Big Data Analytics (BDA) are changing the operations of the water resource management systems. In this study, we present a systematic review, highlighting the contributions of these technologies in water management systems. More specifically, we highlight the IoT and EC water monitoring systems that enable real-time sensing of water quality and consumption. In addition, AI methods for anomaly detection and predictive maintenance are reviewed, focusing on water demand forecasting. BDA methods are also discussed, highlighting their ability to integrate data from different data sources, such as sensors and historical data. Additionally, a discussion is provided of how Water management systems could enhance sustainability, resilience, and efficiency by combining big data, IoT, EC, and AI. Lastly, future directions are outlined regarding how state-of-the-art technologies may further support efficient water resources management. Full article

Federated Learning (FL) offers a promising way to train machine learning models collaboratively on decentralized edge devices, addressing key privacy, communication, and regulatory challenges in smart city environments. This survey adopts a narrative approach, guided by systematic review principles such as PRISMA and Kitchenham, to synthesize current FL research in urban contexts. Unlike prior domain-focused surveys, this work introduces a challenge-oriented taxonomy and integrates an explicit analysis of reproducibility, including datasets and deployment artifacts, to assess real-world readiness. The review begins by examining how FL supports the privacy-preserving analysis of environmental and mobility data. It then explores strategies for resource optimization, including load balancing, model compression, and hierarchical aggregation. Applications in anomaly and event detection across power grids, water infrastructure, and surveillance systems are also discussed. In the energy sector, the survey emphasizes the role of FL in demand forecasting, renewable integration, and sustainable logistics. Particular attention is given to security issues, including defenses against poisoning attacks, Byzantine faults, and inference threats. The study identifies ongoing challenges such as data heterogeneity, scalability, resource limitations at the edge, privacy–utility trade-offs, and lack of standardization. Finally, it outlines a structured roadmap to guide the development of reliable, scalable, and sustainable FL solutions for smart cities. Full article

Predicting urban water consumption helps managers allocate, reserve, and schedule water resources in advance, avoiding supply–demand imbalances. In practical terms, the improved forecasting model can assist urban water managers in planning supply schedules, optimizing reservoir operations, and allocating resources efficiently, thereby supporting sustainable water management in rapidly developing tropical island tourist cities. Traditional forecasting models typically assume that the statistical properties of the data remain stable, an assumption often violated under changing environmental conditions. In addition, tropical island tourist cities have unique hydrological characteristics and frequently fluctuating tourist populations, making water consumption forecasting even more complex in these settings. To address the aforementioned problems, this study develops an improved fractional-order reverse accumulation grey model. Based on the principle of new information priority, the weighted processing of historical data enhances the model’s learning capability for new data. The optimal fractional order is determined using the Greater Cane Rat Algorithm, and the optimized fractional-order reverse accumulation grey model is then applied to forecast water consumption in Sanya City. The results demonstrate that the proposed model achieves a relative error of 4.28% for Sanya’s water consumption forecast, outperforming the traditional grey model (relative error 5.24%), the equally weighted fractional-order reverse accumulation model (relative error 4.37%), and the ARIMA model (relative error 6.92%). The Diebold–Mariano (DM) test further confirmed the statistically significant superiority of the proposed model over the traditional model. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

Reliable dam reservoir operation is crucial for the sustainable management of water resources under climate change-induced uncertainties. This study evaluates four machine learning algorithms—linear regression, decision tree, random forest, and XGBoost—for forecasting daily water levels in a dam reservoir in the Western Black Sea Region of Türkiye. A dataset of 5964 daily hydro-meteorological observations spanning 17 years (2008–2024) was used, and model performances were assessed using MAE, RMSE, and R² metrics after hyperparameter optimization and cross-validation. The linear regression model showed weak predictive capability (R² = 0.574; RMSE = 2.898 hm³), while the decision tree model achieved good accuracy but limited generalization (R² = 0.983; RMSE = 0.590 hm³). In contrast, ensemble models delivered superior accuracy. Random forest produced balanced results (R² = 0.983; RMSE = 0.585 hm³; MAE = 0.046 hm³), while XGBoost achieved comparable accuracy (R² = 0.983) with a slightly lower RMSE (0.580 hm³). Statistical tests (p > 0.05) confirmed no significant differences between predicted and observed values. These findings demonstrate the reliability of ensemble learning methods for dam reservoir water level forecasting and suggest that random forest and XGBoost can be integrated into decision support systems to improve water allocation among agricultural, urban, and ecological demands. Full article

Effective water resource planning is essential for Saudi Arabia, where limited freshwater availability is challenged by rapid population growth, economic development, and climate variability. This study introduces a copula-based modeling framework for forecasting water demand across the country’s urban, industrial, and agricultural sectors. Copulas, compared to traditional models, effectively capture nonlinear and asymmetric relationships among essential variables, including population, temperature, GDP, and sectoral water consumption. Multivariate copula models (Gaussian, Clayton, Gumbel, Frank, t-Copula, and Vine structures) were fitted and evaluated using historical data from 2008 to 2024, obtained from national authorities, including the Ministry of Environment, Water, and Agriculture, the General Authority for Statistics, and the National Center for Meteorology. The 4D normal copula was developed as the most efficient method across all sectors, with MAPE values of 6.37% for urban, 17.51% for industrial, and 23.20% for agricultural consumption. Scenario-based forecasts, which include baseline, high-growth, and sustainability-focused trajectories, indicate that the sustainability scenario yields the best results, resulting in significant demand reductions (21.7% urban, 20.4% industrial, and 8.2% agricultural) with minimal climate impact (+0.4 °C) and the lowest risk levels. The study demonstrates the successful decoupling of water demand from population and economic growth through proper policy interventions, with conditional risk analysis offering actionable early warning capabilities for proactive management. These findings provide a valuable foundation for enhancing national water strategy planning in Saudi Arabia under Vision 2030 and contribute to methodological improvements applicable to water-scarce regions internationally. Full article