Water Consumption Forecasting Research Articles

Unfulfilled water impact forecasts, plans and projects

The article presents a retrospective review of some forecasts, plans and projects of anthropogenic impact on water resources that did not materialize, were not implemented by the planned date or were incomplete. Values of full (water intake) and irrevocable water consumption for 2000, predicted in the 1960s and 1970s by well-known domestic and foreign researchers, are compared with the actual water consumption in 2000 in the world, the USA, and our country. It is shown that most of water consumption forecast parameters turned out to be significantly higher than the actual one, which gives reason to consider these forecasts were not borne out. In the Water Strategy of the Russian Federation, developed in 2009 for the period up to 2020, these values were also significantly overestimated (by tens of percent). Incomplete implementation of various programs did not lead to the expected significant improvement in the water quality of rivers, including the Volga, and reservoirs in Russia. Such failed projects as NAWAPA in the USA and Canada, projects of interzonal redistribution of water resources in the USSR, projects of Nizhneobskaya and Turukhanskaya (Evenkijskaya) hydroelectric power plants, partially implemented projects of Cheboksary and Nizhnekamsk hydroelectric power plants, as well as a number of others are considered. Among the main reasons for unsuccessful forecasts, unfulfilled plans and projects are the lack of reliable data, incomplete knowledge about laws of nature and society development, financial problems, environmental demands, and the dramatically changed economic and political situation.

A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability

AbstractWater and energy have become essential resources and must be wisely managed for a sustainable future. This paper explores the relationship between water consumption and electricity generation in hydropower plants with dams under different climate change scenarios to contribute to the policy perspectives with a new tool and method to sustain the future. Still, as a reliable forecasting tool, the evaluation of the adaptive neuro-fuzzy inference system model has not been tested for forecasting water consumption during electricity generation. Thus, this study uses this modeling approach to generate reliable water consumption estimates based on electricity generation. The operational data of 78 hydroelectric power plants with dams and meteorological parameters were used as input variables, while water consumption was the output parameter in the model. The dataset was randomly divided into training and testing sets, and 85–15% data splitting presented the best-fitted model. The lowest mean average percent error of the hydroelectric power plants' model resulted in 9.59%, and the coefficient of determination of the model was 0.97, which showed that the developed model presented acceptable prediction performance. Various climate change scenarios are applied to analyze the effects of climate parameters on the water consumption of hydropower plants. The annual hydroelectric power plant water consumption and water intensity were estimated between 2,609 million m3 and 4,393 million m3, and 50,768 m3/GWh and 85,487 m3/GWh, respectively, based on climate change scenarios. The study concludes with significant policy suggestions to endorse this approach. Graphical abstract

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.

Machine learning for water demand forecasting: Case study in a Brazilian coastal city

ABSTRACT Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

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.

Urban Water Consumption: A Systematic Literature Review

The study and analysis of urban water consumption habits in different regions contribute to the development of strategies aimed at secure water reduction and distribution. Within this context, knowledge of global water availability and the analysis of factors that influence consumption in different regions in distinct situations become extremely important. Several studies have been carried out in a number of countries and describe different approaches. The objective of this article is to learn about the strategies used in water consumption forecast and analysis. Most of the studies analysed seek to understand the factors influencing consumption in different building types. When it comes to residential buildings, the number of residents and the influence of economic issues on water consumption have an important role in this matter. In this context, pieces of research present the use of awareness campaigns as a strategy towards water use reduction. As a contribution, this article presents a systemic view of the pieces of research conducted and their contribution to forecasting water consumption in different regions. In conclusion, one observes the importance of analysing the factors influencing water consumption in different regions and scenarios, such as during the COVID-19 pandemic. This article can help managers and researchers understand the main factors that influence water consumption and how this consumption takes place in different regions.

Agent Based Modelling in Digital Twins for Household Water Consumption Forecasting

Agent Based Modelling in Digital Twins for Household Water Consumption Forecasting

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.

Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring.

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.

Unobserved heterogeneity and temporal instability in an analysis of household water consumption under block rate pricing

Abstract There is a lack of studies investigating household water consumption while considering the possible heterogeneity in the observed data and its temporal instability, often resulting in inconsistent and biased parameter estimations and, as a result, inaccurate forecasting of household water consumption. To address these constraints, the current study investigates temporal shifts in the household water consumption pattern and the effects of different socioeconomic factors on forecasting household water consumption. Using the results of a household survey performed seasonally in three major cities in Northern Jordan over a 4-year period, separate seasonal models of water consumption are estimated using three alternate modeling approaches to account for possible unobserved heterogeneity. Likelihood ratio tests were performed to investigate the temporal stability of the models' estimations over different seasons across the 4-year period. The findings of these tests indicated that the data are temporally stable over two datasets (the summer and winter seasons). Also, the findings revealed that household water consumption is influenced by a variety of factors, with the impact of many of these factors varying across observations. Finally, the findings highlight the need for additional research into how unobserved heterogeneity can be best modeled in temporal contexts for accurate water consumption forecasting.

Forecasting Water Consumption in the Yangtze River Delta Based on Deformable Cumulative Multivariable Grey Model

The intensified contradiction between water resources and social development has restricted the development of the Yangtze River Delta. Due to the importance of water consumption in relieving this contradiction, this paper proposes a novel cumulative multivariable grey model with a high performance to predict the water consumption. Firstly, the grey correlation analysis is applied to study the influencing factors, and then the DGM(1,N) with deformable accumulation (DDGM(1,N) model) is constructed and used to predict the water consumption. The results show that the resident population has a significant impact on the water consumption, and the performance of the DDGM(1,N) model is better than the other two grey models. Secondly, the proposed novel grey model is applied to predict the water consumption in 17 cities in the Yangtze River Delta, and the predicted water consumption in Zhejiang and Shanghai indicates a downward trend, while the predicated water consumption in some cities of the Anhui Province presents an upward trend, such as Chizhou, Chuzhou, Wuhu and Tongling. Finally, some policy implications are provided that correspond to the population growth and three major industries in different situations. This paper enriches the research method and prediction analysis used for the water consumption, and the findings can provide some decision-making references for water resources management.

Urban daily water demand forecasting

Abstract. Problem. The problem of building models and methods of forecasting the daily need for urban water is considered. Much more attention needs to be given to forecasting methods if utilities are to make decisions that reflect the level of uncertainty precisely in future daily demand forecasts. Daily water consumption, unlike annual and monthly water consumption, is much more highly dependent on chance. Goal. The main goal of this paper is to obtain enough accurate forecasts of daily urban water consumption. Method. An algorithm for calculating the urban daily water demand forecast based on the concept of same-type days of water demand for previous years has been suggested. Scientific novelty. The originality of the method lies in the fact that it does not use neural network models, but still makes it possible to obtain enough accurate forecasts of daily urban water needs. Results. The presented algorithm for calculating the urban daily water demand forecast has been implemented in the form of a software package and has been tested for many years in real-life conditions. The average absolute percentage error of the daily forecast of urban water demand for one month does not exceed 5%. Practical significance. The practical value of this work lies in the fact that the presented software complex for calculating the forecast of the city's daily water demand can be used in the information services of city utility companies to make operational and tactical decisions regarding the provision of water supply services to the population.

A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

Prognoziranje potrošnje vode u Srbiji - ARIMA modelovanje

Water is a symbol of life. Along with air and food, water is a basic existential need. These facts begin to act more strongly on people's opinion and behavior only when he faced with water supply problems. The question is, how to produce more food in these times of growing need for it, while water resources are limited. The Republic of Serbia has a very significant water capacity. Estimation shows that renewable freshwater resources in Serbia are round 55877.93 million of m 3 /per year (Eurostat, 2020). However, water consumption in Serbia is constantly growing, while population is slightly decreasing, which arise the need for time series analysis of water consumption as a tool for future forecast of water consumption.

Consumption Pattern Analysis of Water Consumption For Commercial Connections in Davao City

This study applied the fundamental time series analysis to evaluate the distribution, trend, and later forecast of water consumption for commercial connections by classification of Davao City Water District (DCWD). As such, this study examined the relationship between time and the water consumption of Davao City’s commercial connections over time. The monthly water consumption of commercial connections, categorized by classification per water source systems, was the data source for this study, which ran from January 2015 to December 2019. The four categories of commercial connections’ time series statistics suggested that, from 24 million cubic meters in 2018, commercial consumption dropped to 20 million cubic meters in 2019. This decline is explained by a decrease in consumption of commercials 2.0 and 1.25 classification. The data showed that during the first quarter of the year, increases were observed within five years (short-term). For commercial type 2.0, in 2022, forecasted water consumption would reach 14 million cubic meters in some improved areas of operations. Hence, specific forecasting by type of connection requires in-depth analysis. Thus, further study is required for long-term forecasting.

Water consumption forecasting and pattern classification according to demographic factors and automated meter reading

Water consumption forecasting and pattern classification according to demographic factors and automated meter reading

Industrial water consumption forecasting based on combined CEEMD-ARIMA model for Henan province, central chain: A case study.

Industrial water consumption is a major component of the total regional water consumption. Accurate and scientific prediction of industrial water consumption is an essential guide to the rational use of natural resources. In this paper, we proposed a combined model of CEEMD (collective empirical modal decomposition) and ARIMA (autoregressive integrated moving average) for forecasting industrial water consumption to establish an accurate and efficient forecasting model, because of the poor generalization ability of most current industrial water consumption forecasting models. The influencing factors of industrial water consumption are complex, and the data are non-stationary. "Decomposition-prediction-reconstruction" is one of the significant methods for forecasting time series data, and the data decomposition has a suppressive influence on the modal mixing problem in the EMD decomposition procedure. Based on the smoothing ability of CEEMD for non-smooth signals and the better adaptation of the autoregressive moving average prediction model (ARIMA), a combined CEEMD-ARIMA model was established for industrial water consumption forecasting. This study was conducted for industrial water consumption in Henan Province in central China. The results suggest the combined CEEMD-ARIMA model has a favorable forecasting effect, with an average relative percentage error of 1.96%, and mean square error (MSE) of 0.35, a Nash efficiency coefficient (NSE) of 0.95, a prediction pass rate of 100%, and a better prediction accuracy than the ARIMA model and the combined EEMD-ARIMA model. It provides an effective prediction method for the prediction of industrial water consumption and has good application prospects.

Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia

This study intensively examined the monthly water consumption forecasting performance using advanced time series (ARIMA) models. Thus, this study intends to identify the appropriate ARIMA models to best fit the water consumption data in Southwestern Ethiopia Tepi town and forecast water consumption effectively in the city. The data used for this study was the monthly water consumption in Tepi town from January 2016 to December 2021.The data were converted to returns to enhance their statistical properties and the returns were used to fit a mean equation. The monthly average water consumption in Tepi Town is 77227.8 meters cubic. Both original and transformed data show the trend of water consumption is increasing over time. Several ARIMA models were fitted to the data, and it emerged that the most adequate model for the data was ARIMA (1, 1, 1) based on the model selection criterion. The parameters for ARIMA models were estimated using the Ordinary Least Squares Estimation (OLS) method. The model was used to forecast the consumption for the next ten months and to advise Tepi town Water Company Limited in the city to meet the demand of the people. Conclusion. The consumption of water is increasing from December to September.