Prediction Of Water Consumption Research Articles

Numerical simulations study of concrete mix proportion based on fluidity

Fresh concrete particle packing varies with the composition ratio of the raw materials, and the fluidity changes as a result. The variation in fluidity enhances the complexity of the mix proportion quantitative design, thereby affecting the workability of the concrete. In order to establish the relationship between the mix proportion design parameters and the fluidity, fresh concrete is regarded as a four-level particle size packing consisting of coarse aggregate, fine aggregate, cement and water. Therefore, the dry particle packing theory was extended to the wet particle packing theory, and a physical model for the particle packing of fresh concrete was established. By introducing parameters such as the surplus water consumption, the surplus cement paste and the surplus mortar, a mathematical model for the particle packing of fresh concrete was established. On this basis, the effects of water-cement ratio and water consumption on slump and fluidity were tested under conditions with and without the addition of water-reducing agents, and a functional relationship between slump and water-cement ratio and water consumption was established, which enables the prediction of water consumption at different water-cement ratios and slumps. Furthermore, by establishing the functional relationship between the surplus mortar and the surplus cement paste and the sand to aggregate ratio, the optimal sand to aggregate ratio was functionally predicted. The above research results have enhanced the theory of concrete mix proportion design and assisted in the quantitative design of concrete mix proportion.

Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province.

This study presents a comparative analysis of various Machine Learning (ML) techniques for predicting water consumption using a comprehensive dataset from Kocaeli Province, Turkey. Accurate prediction of water consumption is crucial for effective water resource management and planning, especially considering the significant impact of the COVID-19 pandemic on water usage patterns. A total of four ML models, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), were evaluated. Additionally, optimization techniques such as Particle Swarm Optimization (PSO) and the Second-Order Optimization (SOO) Levenberg-Marquardt (LM) algorithm were employed to enhance the performance of the ML models. These models incorporate historical data from previous months to enhance model accuracy and generalizability, allowing for robust predictions that account for both short-term fluctuations and long-term trends. The performance of each model was assessed using cross-validation. The R2 and correlation values obtained in this study for the best-performing models are highlighted in the results section. For instance, the GBM model achieved an R2 value of 0.881, indicating a strong capability in capturing the underlying patterns in the data. This study is one of the first to conduct a comprehensive analysis of water consumption prediction using machine learning algorithms on a large-scale dataset of 5000 subscribers, including the unique conditions imposed by the COVID-19 pandemic. The results highlight the strengths and limitations of each technique, providing insights into their applicability for water consumption prediction. This study aims to enhance the understanding of ML applications in water management and offers practical recommendations for future research and implementation.

Factors associated with water consumption measured using the stable isotope techniques among Japanese adults: a cross-sectional study.

While some dietary guidelines have established daily water requirements for adults, those for older adults are not well defined. Factors associated with water turnover (WT), indicating water requirements and preformed water (PW), referring to fluids consumed from food and beverages, remain poorly understood. Therefore, we aimed to investigate these factors in Japanese adults. This cross-sectional study included 57 participants aged 54-85 years, living in Fukuoka City, Japan. The WT and PW were measured using the doubly labelled water (DLW) methods from August 2015 to September 2015. Dietary intake and physical activity were evaluated using a 3-day dietary record (DR) and a validated triaxial accelerometer, respectively. Fasting blood and urine samples, as well as environmental conditions, were assessed using a validated technique. We evaluated the variables for predicting water consumption by using a multivariate generalised linear model, with forward stepwise selection using these covariates. The median WT and PW were 2842 ml/day and 2227 ml/day, respectively. In the multivariate model, WT (R2 = 0.629) and PW (R2 = 0.621) were moderately predicted by variables such as sex, body weight, moderate-to-vigorous physical activity, water consumption estimated by DR, urinary aldosterone and osmolality, creatinine clearance and wet-bulb globe temperature. The PW estimates from DR were ~40% lower than those from DLW method, despite a significant correlation between the values. WT and PW are associated not only with lifestyle and environmental factors but also with urinary markers and kidney function. These findings provide useful insights into the differences in each individual's water requirement.

A large-scale evaluation of machine learning algorithms in mid-term water demand forecasting

ABSTRACT Water utilities currently face challenges in accurately measuring user-end consumption as they have to visit every conventional meter that is installed in their service area. This study explores the use of accessible machine learning models to forecast water demand at two crucial temporal scales, addressing practical needs such as customer billing. To draw meaningful conclusions, these models were tested and trained from actual user-end water consumption data from over 2.1 million customers over a 10-year period. These data were provided by the Water and Sewage Company of Greece (EYDAP). The large-scale experiment included the use of statistical models (ARIMA and SARIMA), deep learning [long short-term memory (LSTM)], and clustering techniques (k-NN algorithm). The model with the best performance was ARIMA, outperforming all the other models including the more complex ones. The LSTM model did not perform as expected in predicting water consumption, it excelled only in predicting the total amount of water consumed. These forecasts can be valuable tools for water utility companies, aiding in tasks such as customer billing and water balance calculations. This paper covers all the necessary steps that must be taken to achieve meaningful user-end water consumption forecasts, from raw data preprocessing to hyperparameter tuning for machine learning algorithms.

OPTIMIZATION OF WATER RESOURCES MANAGEMENT USING BIG DATA AND MACHINE LEARNING IN SMART CITIES

In the modern world, the concept of Smart Cities brings new challenges and opportunities for effective resource management and improving the quality of life of citizens. One of the key aspects of Smart Cities is the optimization of water resources management, especially in the context of growing urbanization and a changing climate. Working with big data plays a crucial role in this process, allowing you to analyze and predict water consumption with high accuracy and efficiency. This article examines current methods and technologies for working with big data in the context of predicting water consumption in Smart cities, their advantages and prospects for application in everyday life and urban resource management.

Prediction of water consumption in Beijing based on the multi-variable grey model with adjacent accumulation

Abstract With the rapid development of the social economy, the importance of water resources is becoming increasingly prominent. Urban water demand in Beijing has been growing rapidly. Accurate water consumption forecasting is of utmost importance for reasonable allocation and optimization of water supply systems. In this study, an innovative multi-variable grey prediction model with adjacent accumulation (AOGM(1,N)) is proposed to predict Beijing's annual water consumption for four different water usage scenarios (domestic water, agricultural water, industrial water, and environmental water) by incorporating the adjacent accumulation into the optimized grey model. Grey relational analysis is used to select the key influence factors. The adjustable parameter of the prediction model is chosen by using the particle swarm optimization algorithm. By comparing with other models in the existing literature, the proposed AOGM(1,N) model has evidently superior prediction performance based on the error indicators, which supports the novel method's merits and validity. This study could help us better understand water usage and be applied to the planning and management problems of urban water supply systems.

Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia

Water scarcity is considered a major problem in dry regions, such as the northern areas of Saudi Arabia and especially the city of Hail. Water resources in this region come mainly from groundwater aquifers, which are currently suffering from high demand and severe climatic conditions. Forecasting water consumption as accurately as possible may contribute to a high level of sustainability of water resources. This study investigated different Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting (GB), to efficiently predict water consumption in such areas. These models were evaluated using a set of performance measures, including Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (MedAE). Two datasets, water consumption and weather data, were collected from different sources to examine the performance of the ML algorithms. The novelty of this study lies in the integration of both weather and water consumption data. After examining the most effective features, the two datasets were merged and the proposed algorithms were applied. The RF algorithm outperformed the other models, indicating its robustness in capturing water usage behavior in dry areas such as Hail City. The results of this study can be used by local authorities in decision-making, water consumption analysis, new project construction, and consumer behavior regarding water usage habits in the region.

Projection of ecological water consumption under carbon emission in Chinese provinces

Under the background of “double carbon”, ecological water has become one of the significant resources to ensure the healthy and sustainable development of the regional ecological environment. And the ecological water consumption prediction is a prerequisite for optimizing water supply structures. Considering the impact of CO2 emissions on ecological water consumption, the FGM(1,1) model was firstly used to predict CO2 emissions considered as an impact factor in this paper. Then, an adjacent accumulative grey multivariate convolution model (AGMC(1,N)) was combined with the Average Weakening Buffer Operator (AWBO) to predict the annual ecological water consumption of 30 provinces (municipalities and autonomous regions) in China. The results show that the AGMC(1,N) model combined with the AWBO has better prediction effect. In addition, in the next decade, only six provinces in the northern region will have a decreasing trend in ecological water consumption. However, the other provinces are showing an upward trend. The ecological water consumption trends deviate from the CO2 emission trends in 12 provinces. The research results are beneficial for the formulation of the ecological water consumption policies and the achievement of the dual carbon goals.

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

To reduce the energy consumption of domestic hot water (DHW) production, it is necessary to reasonably select a water supply plan through early predictions of DHW consumption to optimize energy consumption. However, the fluctuations and intermittence of DHW consumption bring great challenges to the prediction of water consumption. In this paper, an ANN-LSTM-A water quantity prediction model based on attention mechanism (AM) enhancement is improved. The model includes an input layer, an AM layer, a hidden layer, and an output layer. Based on the combination of artificial neural network (ANN) and long short-term memory (LSTM) models, an AM is incorporated to address the issue of the traditional ANN model having difficulty capturing the long-term dependencies, such as lags and trends in time series, to improve the accuracy of the DHW consumption prediction. Through comparative experiments, it was found that the root mean square error of the ANN-LSTM-A model was 15.4%, 13.2%, and 13.2% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The corresponding mean absolute error was 17.9%, 11.5%, and 8% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The results showed that the proposed ANN-LSTM-A model yielded better performances in predicting DHW consumption than the ANN, LSTM, and ANN-LSTM models. This work provides an effective reference for the reasonable selection of the water supply plan and optimization of energy consumption.

Artificial neural network model for water consumption prediction in dairy farms

This work presents a model based on artificial neural network (ANN) applied to predict water consumption in Brazilian dairy farms. Inputs were simple process data such as number of lactating cows, milk productivity, type of management, among others, with low computational cost and satisfactory data prediction. Data used for ANN training was acquired during two years from 31 farms in semi-confined dairy production. The analysis of the results was based on the following statistical models’ indicators: R2 (Coefficient of determination), BIAS (trend coefficient), MAE (mean absolute error), RMSE (Root-mean-square deviation), NRMSE (percentage of the mean of the observations) and RAE (Relative absolute error). After performing the ANN training, the results showed good accuracy to predict water consumption in Brazilian dairy farms, with an average absolute error of 28.4% being obtained. On the other hand, considering the dataset used for ANN validation, an average absolute error of 48% was obtained.

Medium-term water consumption forecasting based on deep neural networks

Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.

Water consumption time series forecasting in urban centers using deep neural networks

The time series analysis and prediction techniques are highly valued in many application fields, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for different time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifically designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.

Machine learning assisted water management strategy on a self-sustaining seawater desalination and vegetable cultivation platform

To enable vegetable cultivation in conditions lacking freshwater and soil, such as islands and offshore platforms, this study proposes to combine interfacial solar evaporation with hydroponic techniques to set up an electricity-free self-sustaining desalination and cultivation platform. To maintain a dynamic balance between freshwater supply and demand on the platform, we investigate the relationship between water consumption in hydroponic lettuce cultivation and meteorological conditions such as temperature, humidity, and solar radiation. The limitations of traditional evapotranspiration models like the FAO-PM model in practical hydroponic cultivation is highlighted and four machine learning algorithms, including Random Forest, Backpropagation Neural Network, Support Vector Regression, and N-BEATS, are developed to predict water consumption specifically for hydroponic cultivation. The Random Forest model, selected based on performance comparison, achieves MAPE and R2 values of 10.05% and 0.974 respectively on the test set. Using meteorological data from Shanghai in 2022, the feasibility of the platform is assessed, demonstrating a theoretical lettuce yield of 260 plants/m2/year for solar stills on a unit area without water shortages throughout the year. This study verifies the feasibility and stability of the hydroponic cultivation platform, and is promising for the development of sustainable agriculture in offshore scenarios with limited freshwater and soil resources.

Domestic water consumptions and associated factors in rural household of Harari region, Eastern Ethiopia; A cross sectional study.

Sustainable rural water supply management requires accurate estimations of water consumption and understanding factors influencing consumption. Climate change exacerbates challenges, especially in developing countries with high poverty and limited access to clean water. Ethiopia has the lowest municipal water supply in Africa. The residents of the Harar region currently get water once a week or two weeks for just a few hours, which is very inadequate for everyday household activities like drinking, cooking, cleaning utensils, washing clothes, etc. Despite global efforts to identify the factors that affect domestic water consumption, those related to domestic water use in rural regions have not been sufficiently studied, particularly in rural woredas of Harari region, Ethiopia. To assess the domestic water consumption and associated factors at household level in rural woredas of Harari region, Eastern Ethiopia from October 29, 2022 to November 21, 2022. A community-based cross-sectional study was conducted on 408 households using stratified sampling techniques, collecting data on socio-demographic and water use characteristics. Epi-Data and SPSS were used for data analysis. Descriptive analysis was used to determine average total and per capita water consumption, while a multiple linear regression model allowed for the identification of associated factors and the prediction of water consumption with statistical significance. Daily water consumption was found to be 103.3 L/hh/d and 17.6 L/c/d. The associated factors included the main source of water, time of collection, household size, wealth status, water price, and frequency of gathering water. Thus, the provision of an adequate quantity of water for rural households should be given attention for maintaining human wellbeing, and the consideration of socio-economic factors affecting per capita water consumption is desirable in the planning and implementation of proper water demand management strategies.

Smart metering and Internet of Things for efficient water management

Global water use has grown steadily in the past decade, and climate change will further worsen the levels of water stress. Another global issue lies in the ageing of water infrastructures, registering conspicuous water losses (40% in the Italian scenario). A great opportunity is represented by the exploitation of new technologies relating to IoT, namely smart meters, which can enable the prediction of water consumption, detect leaks, and customise the service. This paper assesses the economic and environmental impact of smart meters adoption for water consumption, by taking the Italian scenario as a reference context, through the development of an analytical model which considers the benefits and costs associated with their adoption, with the ultimate objective of evaluating the convenience of the investment. Data to feed the model was collected through secondary sources, literature reviews, and interviews with utility companies’ employees. Results provide corroborating evidence of the positive impact of smart water meter adoption, both in economic and environmental terms, in particular by increasing the roll-out number, given the higher amount of data available and economies of scale to be exploited. The present study contributes to the academic literature by providing a comprehensive model that considers economic and environmental aspects of smart water adoption, which allows practitioners to have an insightful understanding of the involved variables in such investments.

Predicting tomato water consumption in a hydroponic greenhouse: contribution of light interception models

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics’ highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models’ main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant’s dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management.

Residential water and energy consumption prediction at hourly resolution based on a hybrid machine learning approach

Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are often based only on historical consumption data for the resource being predicted, with activity or appliance information and household attribution as additional information. Few studies have used aggregated water and energy consumption for predictions. Within this context, the current study proposed a novel hybrid machine learning model based on the Prophet time-series model, Gated Recurrent Unit network, and self-adaptive weights, called the Prophet-GRU model, which could jointly include historical water and electricity consumption as inputs for hourly water or electricity prediction. Data on hourly water and electricity consumption in six households in Beijing during January–March 2020 were used to train and validate the Prophet-GRU model. The goodness of fit indicator (R2) and prediction accuracy (mean squared error and mean absolute error) for the water and electricity predictions were evaluated. Compared with the single input of water or electricity, with the combined input of data of these two resources, the proposed Prophet-GRU model achieved improvements of 29.2 % and 48.5 % in R2, for water and electricity consumption prediction, respectively. Our results could help better understand water-energy linkages and promote collaborative water and energy management practices.

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water consumption forecasts focus on historical data or single water use influencing factors; moreover, daily water consumption could be influenced by meteorologic conditions. The influence of complex meteorology factors on water consumption is analyzed based on a gradient-boosted regression tree (GBRT) model. The correlation of 10 meteorologic factors has been discussed and divided into 5 categories, including temperature factor, pressure factor, precipitation factor, sunshine factor, and wind factor. Through the GBRT algorithm, the daily water consumption of residential area could be predicted with a maximum error of ±8%. The results show that the average ground temperature (the feature importance accounts for 81% of the total) has the greatest impact on the daily water consumption of the residential community, followed by the somatosensory temperature (the feature importance accounts for 7% of the total). The method can provide the daily water consumption of water consumption nodes with higher precision for municipal water supply network model accuracy. It also provides a reference for water utility operation schemes and urban development planning.

Urban Water Consumption Prediction Based on CPMBNIP

Urban Water Consumption Prediction Based on CPMBNIP

A fuzzy cognitive map-based algorithm for predicting water consumption in Spanish healthcare centres

The management of water consumption in healthcare centres can have positive impacts on both the environmental performance and profitability of health systems. Computational tools assist in the decision-making process of managing the operation and maintenance of healthcare centres. This research aimed to integrate the empirical knowledge of experts in Healthcare Engineering and the historical data from 66 healthcare centres in a Fuzzy Cognitive Map. The outputs of the predictive model included water consumption, water cost, and CO2 emissions in healthcare facilities, along with eleven variables to discover the causes and consequences of water consumption in healthcare centres. A healthcare centre with about 12 350 users, located in a city that experiences an average of 1100 heating degree days, whose facilities be moderately energy-efficient contributing over 50% with renewable energies is expected to consume 8.4 dam3 of water with 32.1 k€ of cost, and contribute realising 30.8 ton CO2eq emissions. The use of Fuzzy Cognitive Maps for prediction can provide a high level of effectiveness in identifying the factors that contribute to water consumption and in designing key performance indicators to manage the environmental performance of healthcare buildings. This tool is extremely effective in enhancing the performance of the management division of health systems.