Search Results (28)

Reducing energy consumption while maintaining stable water quality remains a major challenge in ornamental aquaculture. This study proposes an integrated predictive and energy-aware aquaculture management framework combining Internet of Things (IoT) sensing, Long Short-Term Memory (LSTM)-based prediction, Digital Twin (DT) simulation, and Cyber-Physical System (CPS) control. Real-time sensor networks monitored dissolved oxygen (DO), ammonia (NH₃), temperature, pH, turbidity, and energy consumption in a koi pond over a 45-day deployment period. Forecasted environmental states generated by the LSTM model were validated through a physics-informed Digital Twin prior to actuator execution to improve operational reliability and control safety. Experimental results demonstrated strong agreement between the Digital Twin and observed pond dynamics, achieving R² values of 0.97 for dissolved oxygen and 0.94 for ammonia. Compared with conventional manual operation, the proposed smart predictive control mode reduced total energy consumption by 26.86%. Statistical analysis confirmed that the reduction was highly significant (p < 0.001), with average daily energy consumption decreasing from 212 ± 6.06 Wh/day under manual operation to 154.71 ± 4.52 Wh/day under smart predictive control. Full article

Smart water grid technologies have been widely adopted as a key component of digital transformation in water resource management, where real-time household water consumption data collected from smart water meters serve as fundamental inputs. However, these datasets often contain numerous outliers and missing values due to communication errors, which degrade data reliability and hinder accurate analysis. This study proposes an improved framework for outlier detection and missing data imputation tailored to the characteristics of cumulative household water consumption data. The proposed imputation methods were evaluated against conventional approaches using error metrics, and the results demonstrated significant improvements in accuracy, with RMSE values substantially lower than those of the reference method. In addition, prediction models with varying levels of complexity were explored to examine how improved data quality influences forecasting performance. The results indicate that, although data preprocessing enhances data reliability, prediction performance remains limited due to the inherent variability and stochastic nature of household water consumption data. Prediction models with varying levels of complexity were constructed and evaluated using the corrected datasets. The performance of the models varied depending on dataset characteristics, and no single model consistently outperformed others. Overall, this study highlights the critical role of data quality improvement in smart water management systems and provides practical insights into missing data imputation, while suggesting that further advancements in prediction require additional explanatory variables and more sophisticated modeling approaches. 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

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. 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

As the global demand for water rises and climate pressures intensify, projections indicate that water scarcity will impact nearly 40% of the world’s population by 2030 and a quarter of all children by 2040. This study reviews the current literature on residential water efficiency, highlighting the most effective strategies for reducing water waste. A systematic literature review—guided by transparent criteria and quality assessments using the Critical Appraisal Skills Program (CASP)—was conducted to extract insights into water distribution management strategies. This study examines current smart water management initiatives aimed at reducing waste, with a particular focus on the policy and regulatory drivers behind global water conservation efforts. Furthermore, it shows innovative smart solutions such as Artificial Intelligence (AI)-powered forecasting, Internet of Things (IoT)-based metering, and predictive leak detection, which have demonstrated reductions in residential water loss by up to 30%, particularly through real-time monitoring and adaptive consumption strategies. The study concludes that innovative technologies must be actively supported and implemented by governments, utilities, and global organizations to proactively reduce water waste, safeguard future generations, and enable data-driven, AI-powered policy and decision-making for improved water use efficiency. Full article

Achieving both indoor environmental quality (IEQ) and energy efficiency in school buildings remains a challenge, particularly in older structures where renovation strategies often lack site-specific validation. This study evaluates the impact of energy retrofits on a 1970s primary school in France by integrating in situ measurements with a validated numerical model for forecasting energy demand and IEQ. Temperature, humidity, and CO₂ levels were recorded before and after renovations, which included insulation upgrades and an air handling unit replacement. Results indicate significant improvements in winter thermal comfort (PPD < 20%) with a reduced heating water temperature (65 °C to 55 °C) and stable indoor air quality (CO₂ < 800 ppm), without the need for window ventilation. Night-flushing ventilation proved effective in mitigating overheating by shifting peak temperatures outside school hours, contributing to enhanced thermal regulation. Long-term energy consumption analysis (2019–2022) revealed substantial reductions in gas and electricity use, 15% and 29% of energy saving for electricity and gas, supporting the effectiveness of the applied renovation strategies. However, summer overheating (up to 30 °C) persisted, particularly in south-facing upper floors with extensive glazing, underscoring the need for additional optimization in solar gain management and heating control. By providing empirical validation of renovation outcomes, this study bridges the gap between theoretical predictions and real-world effectiveness, offering a data-driven framework for enhancing IEQ and energy performance in aging school infrastructure. Full article

This article is devoted to the determination of the average daily electric load and the average electric load during the hours of maximum load, taking into account the generalized coefficient A_i, using data on electricity consumption for apartment buildings and individual residential buildings in Chelyabinsk and the cities of Dushanbe and Khorog in the Republic of Tajikistan. The results of modeling the average daily electric load, taking into account the developed generalized coefficient A_i, showed that the specific power values for apartments in apartment buildings and in individual residential buildings in the city of Chelyabinsk and the cities of Dushanbe and Khorog of the Republic of Tajikistan were overestimated, taking into account the applicability in the Republic of Tajikistan of the same standard values of specific electric loads (SELs) for apartments in apartment buildings (ABs) as in the Russian Federation. According to the results of modeling using data on the average monthly electricity consumption for 226 apartments in ABs and for individual residential buildings in Chelyabinsk, and according to the proposed approach, the average daily electric load on days during the month varied in the range of 2–3.5 kW/sq and below, while that for the cities of Dushanbe and Khorog of the Republic of Tajikistan varied in the range of 2–5 kW/sq and below, which did not exceed the SEL given by RB 256.1325800.2016. However, because of the lack of other energy sources (gas supply and hot water supply) in the conditions of the Republic of Tajikistan, on the basis of the obtained maximum load time factor and the generalized coefficient ${\mathrm{A}}_{\mathrm{i}\left(\mathrm{E}\right)}$, the obtained values of actual capacity exceeded the maximum during peak hours by 1.2–2.5 times the SEL given by RB 256.1325800.2016. To increase the durability and serviceability of power supplies and enhance the effectiveness of forecasting, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its own regression model. The decrease in mean absolute error due to clustering was 0.52 MW (57%). The use of meteorological conditions allowed the forecast error to be reduced by 0.22 MW (27%). High accuracy in electrical consumption forecasting leads to increased quality of power system management in general, including under such key indicators as reliability and serviceability. Full article

As a clean energy source, shale gas plays a crucial role in mitigating the supply–demand imbalance of natural gas and in facilitating the transition to a low-carbon economy. This study employs a system dynamics model to forecast future production trends in shale gas in China, analyze its implications for the natural gas supply–demand structure, and explore pathways for sustainable development. Firstly, by integrating the characteristics of China’s shale gas resources, market dynamics, and policy frameworks, the key factors influencing production are identified, and their interrelationships are systematically analyzed. Subsequently, a causal loop diagram is constructed using the VENSIM software(VENSIM PLE 9.3.5 x64), a set of representative variables is selected, and the logical relationships among these variables are established through a multivariate statistical analysis, culminating in the development of a production forecasting model for China’s shale gas (stock and flow diagram). Finally, based on parameter assumptions, this study predicts the production trends in shale gas in China under multiple scenarios. The forecasting results reveal that China’s shale gas production is expected to grow at an average annual rate of 3.32% to 8.02%, with production under the reference scenario projected to reach 724.22 × 10⁸ m³ by 2040. However, the growth of shale gas production over the next two decades remains limited, accounting for a maximum of 12.07% of the total natural gas consumption, underscoring its transitional role in the low-carbon transformation. To address these challenges, this study proposes four policy recommendations: (1) prioritize the development of shallow, high-quality gas-bearing blocks while gradually transitioning to deeper formations; (2) intensify technological innovation in deep shale gas extraction to enhance recovery rates and mitigate production decline rates; (3) implement flexible production subsidies and moderately increase natural gas sales prices to incentivize production and optimize resource allocation; and (4) strengthen ecological conservation and improve water resource management to ensure the sustainable development of shale gas. Full article

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R² and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management. Full article

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization. Full article

This paper considers bottled water with respect to the three pillars of sustainability: economic viability, environmental impacts, and social equity. Per-capita consumption of bottled water has been growing steadily and is the fastest-growing sector of the packaged beverages industry, with expected annual growth of 10% until 2026. Most bottled water is sold in PET containers, and various impacts are evident along all phases of the product lifecycle. This paper reviews market trends and forecasts, lifecycle estimates of energy consumption, associated air pollution and GHG emissions, water footprint, and waste generation. Concerns around human and ecosystem health due to pollution, land use changes, storage conditions, microplastics, and leaching from containers are described, as well as local environmental benefits from companies’ efforts to preserve the quality of their source water. Growing awareness of the cumulative negative impacts of bottled water have pushed the industry to voluntarily improve its performance. Yet, as growth continues, further actions should focus on stricter regulation and on the provision of more sustainable, affordable, available, and trusted alternatives. Gaps remain in knowledge of the effects of bottled water over its full life cycle. Full article

Groundwater is regarded as the primary source of agricultural and drinking water in semi-arid and arid regions. However, toxic substances released from sources such as landfills, industries, insecticides, and fertilizers from the previous year exhibited extreme levels of groundwater contamination. As a result, it is crucial to assess the quality of the groundwater for agricultural and drinking activities, both its current use and its potential to become a reliable water supply for individuals. The quality of the groundwater is critical in Egypt’s Sohag region because it serves as a major alternative source of agricultural activities and residential supplies, in addition to providing drinking water, and residents there frequently have issues with the water’s suitability for human consumption. This research assesses groundwater quality and future forecasting using Deep Learning Time Series Techniques (DLTS) and long short-term memory (LSTM) in Sohag, Egypt. Ten groundwater quality parameters (pH, Sulfate, Nitrates, Magnesium, Chlorides, Iron, Total Coliform, TDS, Total Hardness, and Turbidity) at the seven pumping wells were used in the analysis to create the water quality index (WQI). The model was tested and trained using actual data over nine years from seven wells in Sohag, Egypt. The high quantities of iron and magnesium in the groundwater samples produced a high WQI. The proposed forecasting model provided good performances in terms of average mean-square error (MSE) and average root-mean-square error (RMSE) with values of 1.6091 × 10⁻⁷ and 4.0114 × 10⁻⁴, respectively. The WQI model’s findings demonstrated that it could assist managers and policymakers in better managing groundwater resources in arid areas. Full article

Water shortage has been an issue for urbanized areas. For the Penang state in Malaysia, it is forecast that there will be a significant increase in water demand in the future. Penang authorities in Malaysia are trying to find an alternative water source to overcome the problem, with one of the options being the Perai River catchment. However, the river water quality was found to be polluted and not suitable to be used for water extraction for domestic consumption. This paper aims to study the pollution level variation due to changes in rainfall during the year in the Perai River Basin, and estimate the TMDL of the river in a particular case for BOD, COD, and NH₃N parameters. A water quality model was developed for the Perai River, Jarak River and Kulim River using InfoWorks ICM. The year 2016 was selected as a model event due to data availability. BOD, COD and NH₃N concentrations were used for TMDL calculation, and the load duration curve approach was used to estimate TMDL. The tidal effect at the downstream of the Perai River was found to impact the data analysis in the river stretch. It was found that pollutant load exceedance was the highest during the rainy season and the problematic pollutant was NH₃N. Thus, local authorities need to focus on tidal and seasonal change factors when developing action plans to manage water quality issues in this basin. Full article

A region’s population growth inevitably results in higher water consumption. This persistent rise in water use increases the region’s wastewater production. Consequently, due to this increase in wastewater (influent), Wastewater Treatment Plants (WWTPs) are required to run effectively in order to handle the huge demand for treated/processed water (effluent). Knowing in advance the influent and effluent parameters increases the operational efficiency and enables cost-effective utilization of diverse resources at wastewater treatment plants. This paper is based on a prediction/forecasting of an influent quality parameter, namely total MLD, as well as effluent quality parameters, namely MPN, BOD, DO, COD and pH for the real-time data collected pre-, during and post-COVID-19 at the Bharwara WWTP in Lucknow, India. It is the largest UASB-based wastewater treatment facility in Uttar Pradesh and the second largest in Asia. In this paper, we propose a novel model namely, wPred comprising extensions of SARIMA with seasonal order and ANN-based ML models to estimate the influent and effluent quality parameters, respectively, and compare it with the existing machine learning models. The lowest sMAPE error for the influent parameters using wPred is 2.59%. The findings of the paper show a strong correlation (R-value), up to 0.99, between the effluent parameters actually measured and predicted. As a result, the model designed in this paper has an acceptable level of accuracy and generalizability which efficiently predicts/forecasts the performance of Bharwara WWTP. Full article