Prediction Of Water Consumption Research Articles

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.

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.

Forecasting of water consumption by integrating spatial and temporal characteristics of short-term water use in cities

Urban water consumption prediction is an important basis for urban water resource management and optimal scheduling of water supply. Most existing prediction methods disregard the spatial–temporal characteristics of water consumption data and utilize incomplete data mining, resulting in prediction models with low accuracy. To accurately predict short-term water demand, a data-cleaning algorithm based on statistical distributions and velocity constraints is proposed in this study. The long- and short-term time-series network (LSTNet), automated spatial–temporal graph prediction (AutoSTG), and attention-based spatial–temporal graphical convolutional neural network (ASTGCN) deep learning models were investigated for automatically capturing the spatial–temporal dynamic features. Additionally, the short-term predictions for 1, 3, 6, 12, and 24 h forecasting periods were investigated based on the water consumption data of water plants in Tianjin. The results revealed that the proposed data-cleaning algorithm can effectively manage single-point anomalies and continuous outliers, thus improving prediction accuracy. The LSTNet, AutoSTG, and ASTGCN deep learning models, particularly the ASTGCN model based on the spatial–temporal attention mechanism, significantly improved the prediction accuracy and adaptive spatial–temporal feature extraction. The final prediction model that combines data-cleaning algorithms with the ASTGCN deep learning method achieved root mean square error, mean absolute error, accuracy, and relative squared error values of 566.4 m3, 377.7 m3, 0.964, and 0.112, respectively. The proposed forecasting framework can improve short-term water use forecasting at multiple sites in the city and provide support for fine-grained water allocation.

A BPNN-based ecologically extended input-output model for virtual water metabolism network management of Kazakhstan.

In this study, a back-propagation-neural-network-based ecologically extended input-output model (abbreviated as BPNN-EIOM) is developed for virtual water metabolism network (VWMN) management. BPNN-EIOM can identify key consumption sectors, simulate performance of VWMN, and predict water consumption. BPNN-EIOM is then applied to analyzing VWMN of Kazakhstan, where multiple scenarios under different gross domestic production (GDP) growth rates, sectoral added values, and final demands are designed for determining the optimal management strategies. The major findings are (i) Kazakhstan typically relies on net virtual water import (reaching 1497.9 × 106 m3 in 2015); (ii) agriculture is the major exporter and advanced manufacture is the major importer; (iii) by 2025, Kazakhstan's water consumption would increase to [19322, 22016] × 106 m3 under multiple scenarios; (iv) when Kazakhstan's GDP growth rate, manufacturing's added value, and final demand are scheduled to 5.5%, 8.5%, and 5.8%, its VWMN can reach the optimum. The findings are useful for decision makers to optimize Kazakhstan's industrial structure, mitigate the national water scarcity, and promote its socio-economic sustainable development.

A review on smart water management in various domestic areas: an approach for water consumption and leakage perspectives

One of the major issues that is affecting the water utilities in domestic areas is leakages through the distribution networks, non-revenue water estimation, and failure in the prediction of water consumption at individual spaces. This review paper basically focuses on these three important directions to understand the feasible domestic water management systems with integrated models of internet of things and artificial intelligence approaches. Different smart water management techniques have been reported in the literature to improve efficiency and avoid non-revenue water issues, and these are consolidated in this paper. Prediction failure of water utility in the water distribution systems is also presented in this paper. This article summarises a measure of water consumption statistics, a detailed summary of non-revenue water, and leak detection technologies at extensive areas to provide a wide sense of understanding for the prediction of the water consumption and leakage detection.

Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province.

Water consumption prediction is an integral part of water resource planning and management. Constructing a highly precise water consumption prediction model is of great significance for promoting regional water resource planning and high-quality development of the socio-economy. This paper focuses on the case of the typical karst region in Guizhou Province in China. Based on data on water consumption and its influencing factors spanning 2000-2020, the principal component analysis method was applied to reduce the dimensionality of 16 influencing factors of water consumption in Guizhou; the principal components extracted were used as input samples of the BP neural network and a PCA-BP neural network water consumption prediction model was conducted to predict water consumption of Guizhou Province in the next 10years. The results show that the mean absolute error and mean relative error of prediction based on the constructed PCA-BP neural network were 2.8% and 2.9%, respectively, with superior performance in terms of prediction error and trends compared with other models. This paper discusses the main influencing factors of water consumption and analyzes their influence on the water consumption forecasting model so that the parameters of the water consumption forecasting model can be selected more efficiently and provide a reference for regional water consumption analysis and water resource planning and management.

When will China's total water consumption reach the turning point? EKC simulation and influencing factors.

The turning point of China's total water consumption is very important for the understanding of the evolution trend of total water consumption and the formulation of water conservation policies. Based on the environmental Kuznets curve (EKC) model, this paper verifies the shape of water consumption Kuznets curve. Scenario analysis and Monte Carlo simulation are combined for the first time to predict water consumption Kuznets curve. The LMDI model is used to decompose the driving factors of the evolution of total water consumption, and the STIRPAT model is expanded to explore the influence mechanism of total water consumption. The results show the following: (1) The theoretical water consumption Kuznets curve exists, and the turning point is 26,448 yuan RMB (in around 2013). (2) Based on the multiple driving factors (water intensity, per capita GDP, and population) and multiple scenarios (baseline scenario, target scenario, and 2 adjusted scenarios), 32 scenarios are designed in this paper; in the S1-S8, the turning point still appeared near 2013; the curves under the S11-S14, S16, and S25-S32 were inverted U-shaped, and the turning point was 48,728 yuan RMB (in around 2025); and in the S9, S10, and S15, the curve shows an upward trend; in the S17-S24, the curve has a rising-falling-rising characteristic. (3) Domestic effect and ecological effect both play a role in promoting the total water consumption, while the production effect is in an inverted N-shaped. Economic growth has always promoted the increase in industrial and agricultural water consumption, and the role of population size is relatively weak. The intensity of production water consumption has always promoted reduction in industrial and agricultural water consumption. Industrial water intensity and industrial structure are the primary and secondary factors that promote the decline of production intensity. (4) The per capita GDP has the largest contribution to total water consumption, followed by the water intensity, and the industrial structure has the least impact. The population has a negative impact. Based on this, a number of policy implications are obtained.

Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering

Enhanced weathering (EW) of minerals could potentially absorb atmospheric CO2 at gigaton scale per year and store it as bicarbonate and carbonate in the ocean. However, this process must be accelerated by engineered reactors, in which optimal reaction conditions maximise the CO2 capture rate and minimise the energy and water consumption. In this work, trickle beds (TBs) and packed bubble columns (PBCs), operated with fresh water and CO2 -rich flue gas, are chosen as typical chemical reactors to perform the EW-based CO2 capture. We firstly develop experimentally validated physics-based mechanistic models then generate data to train data-driven surrogate models to achieve rapid prediction of performance and multi-variable optimization. Two surrogate models, namely, response surface methodology (RSM) and extended adaptive hybrid functions (E-AHF), are developed and compared, in which the effect of five design variables on three objective functions are investigated. Results show that the R2 for the prediction of CO2 capture rate (CR) and water consumption (WC) through RSM and E-AHF is higher than 0.84. For TB reactors, in particular, the calculated R2 is higher than 0.96. The prediction accuracy of energy consumption (EC) through the RSM approach is, however, relatively poor (R2 ∼ 0.79), but is improved by using the E-AHF surrogate model, increasing to R2 ∼ 0.89. The developed data-driven surrogate model can rapidly predict the performance indicators of TB and PBC reactors without solving complex mechanistic models consisting of many partial differential equations. After optimization using the surrogate models, improvements were achieved in the objectives for TB and PBC reactors as follows: CR increased by 37.8 % and 13.1 %, EC reduced by 37.4 % and 23.8 %, and WC reduced by 12.5 % and 40.7 %, respectively.

An Artificial Neural Network Model for Water Quality and Water Consumption Prediction

With rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches.