Short-term Water Demand Forecasting Research Articles

A Machine Learning Framework for Enhancing Short-Term Water Demand Forecasting Using Attention-BiLSTM Networks Integrated with XGBoost Residual Correction

Accurate short-term water demand forecasting assumes a pivotal role in optimizing water supply control strategies, constituting a cornerstone of effective water management. In recent times, the rise of machine learning technologies has ushered in hybrid models that exhibit superior performance in this domain. Given the intrinsic non-linear fluctuations and variations in short-term water demand sequences, achieving precise forecasts presents a formidable challenge. Against this backdrop, this study introduces an innovative machine learning framework for short-term water demand prediction. The maximal information coefficient (MIC) is employed to select high-quality input features. A deep learning architecture is devised, featuring an Attention-BiLSTM network. This design leverages attention weights and the bidirectional information in historical sequences to highlight influential factors and enhance predictive capabilities. The integration of the XGBoost algorithm as a residual correction module further bolsters the model’s performance by refining predicted results through error simulation. Hyper-parameter configurations are fine-tuned using the Keras Tuner and random parameter search. Through rigorous performance comparison with benchmark models, the superiority and stability of this method are conclusively demonstrated. The attained results unequivocally establish that this approach outperforms other models in terms of predictive accuracy, stability, and generalization capabilities, with MAE, RMSE, MAPE, and NSE values of 544 m3/h, 915 m3/h, 1.00%, and 0.99, respectively. The study reveals that the incorporation of important features selected by the MIC, followed by their integration into the attention mechanism, essentially subjects these features to a secondary filtration. While this enhances model performance, the potential for improvement remains limited. Our proposed forecasting framework offers a fresh perspective and contribution to the short-term water resource scheduling in smart water management systems.

Short term water demand forecast modelling using artificial intelligence for smart water management

Water is an important resource for life and its existence. Water demand is increasing with increasing economic growth and population, while the water availability is continually depleting making an increasing stress on freshwater resources, necessitating monitoring of water consumption. In addition to controlling the water supply with an efficient water management system, automating the system in terms of both monitoring and operation has received a lot of attention in recent years. Short-term water demand forecast aids in the optimal control of a water supply system and its accurate forecasting helps in reducing operating costs and saving energy. Despite extensive research, the use of demand forecasting for efficient water management has yet to be implemented in India. As a result, the focus of this research is primarily on the model for forecasting short-term water demand using artificial intelligence. A comparative study has been carried out between nine machine learning and deep learning models using the water consumption data over the period from 2020 to 2021 for the city of Hubli in Karnataka. Univariate and multivariate time series forecasting models were considered using the 10-min interval flow meter readings to find the most suitable predictive model. For univariate time series forecasting, only the water consumption was used to predict the water demand, whereas, for the multivariate model, climatic parameters, and calendar inputs (like an hour of the day, holidays, etc.) were considered along with the water consumption data. The results suggest that the deep learning models outperformed the machine learning models, and Long-Short Term Memory (LSTM) model demonstrates the best prediction performance in the two scenarios with a mean absolute error of 0.11 m3/hr for univariate model and 2.96 m3/hr for the multivariate model. The best predictive model can be used to forecast the short-term water demand for any region to ensure sustainable water resource management.

A weighting strategy to improve water demand forecasting performance based on spatial correlation between multiple sensors

Graph convolutional network (GCN) has been used to capture spatial correlation between multiple sensors for better performance of short-term water demand forecasting, which is essential for the implementation of smart water such as optimal scheduling and anomaly detection. However, the GCN assumes every sensor's importance to be the same and describes the spatial correlation purely from a data perspective. To resolve the two issues that affect prediction accuracy, this study proposes a weighting strategy and develops a spatial correlation-based GCN (SCGCN) prediction model. Self-attention mechanism is used to comprehensively analyze water demand data of every sensor and flow resistance between sensors (i.e., head loss along pipes), generating spatial correlation coefficients with the consideration of hydraulics. Then the coefficients are used as weights to aggregate neighboring sensors’ data, extracting accurate input features for the SCGCN model. The study utilized real monitoring data to develop the SCGCN model and compare it with a traditional artificial neural network (ANN) model. Results show that the SCGCN model can outperform the ANN model, especially for multi-step prediction. The root mean square errors of 30, 45, and 60 min multi-step prediction cases are reduced by 4.4% to 9.2% for different sensors.

Short-term water demand forecasting using data-centric machine learning approaches

Abstract Accurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focused on model-centric approaches, where various forecasting models are tested to improve accuracy. The study undertakes a data-centric machine learning approach by analysing the impact of training data length, temporal resolution and data uncertainty on forecasting model results. The models evaluated are Autoregressive (AR) Integrated Moving Average (ARIMA), Neural Network (NN), Random Forest (RF) and Prophet. The first two are commonly used forecasting models. RF has shown similar forecast accuracy to NN but has received less attention. Prophet is a new model that has not been applied to short-term water demand forecasting, though it has had successful applications in various fields. The results obtained from four case studies show that (1) data-centric machine learning approaches offer promise for improving forecast accuracy of short-term water demands; (2) accurate forecasts are possible with short training data; (3) RF and NN models are superior at forecasting high-temporal resolution data; and (4) data quality improvement can achieve a level of accuracy increase comparable to model-centric machine learning approaches.

MACLA-LSTM: A Novel Approach for Forecasting Water Demand

Sustainable and effective management of urban water supply is a key challenge for the well-being and security of current society. Urban water supply systems have to deal with a huge amount of data, and it is difficult to develop efficient intervention mechanisms by relying on the human experience. Deep learning methods make it possible to predict water demand in real-time; however, deep learning methods have a large number of hyperparameters, and the selection of hyperparameters can easily affect the accuracy of prediction. Within this context, a novel framework of short-term water demand forecast is proposed, in which a forecasting method clouded leopard algorithm based on multiple adaptive mechanisms—long short-term memory networks (MACLA-LSTM)—is developed to improve the accuracy of water demand predictions. Specifically, LSTM networks are used to predict water demand and the MACLA is utilized to optimize the input parameters of the LSTM. The MACLA-LSTM model is evaluated on a real dataset sampled from water distribution systems. In comparison with other methods, the MACLA-LSTM achieved MAE values of 1.12, 0.89, and 1.09; MSE values of 2.22, 1.21, and 2.38; and R2 values of 99.51%, 99.44%, and 99.01%. The results show the potential of the MACLA-LSTM model for water demand forecasting tasks and also demonstrate the positive effect of the MACLA on forecasting tasks by comparing results with LSTM variant models. The proposed MACLA-LSTM can provide a resilient, sustainable, and low-cost management strategy for water supply systems.

A neural network approach for short-term water demand forecasting based on a sparse autoencoder

Abstract This paper presents a backpropagation neural network (BPNN) approach based on the sparse autoencoder (SAE) for short-term water demand forecasting. In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. After that, the extracted information is employed to optimize the initial weights and thresholds of the BPNN. In addition, to enhance the effectiveness of the proposed method, data reconstruction is implemented to create suitable samples for the BPNN, and the early stopping method is employed to overcome the BPNN overfitting problem. Data collected from a real-world water distribution system are used to verify the effectiveness of the proposed method, and a comparison with the BPNN and other BPNN-based methods which integrate the BPNN with particle swarm optimization (PSO) and the mind evolutionary algorithm (MEA), respectively, is conducted. The results show that the proposed method can achieve fairly accurate and stable forecasts with a 2.31% mean absolute percentage error (MAPE) and 320 m3/h root mean squared error (RMSE). Compared with the BPNN, PSO–BPNN and MEA–BPNN models, the proposed method gains MAPE improvements of 5.80, 3.33 and 3.89%, respectively. In terms of the RMSE, promising improvements (i.e., 5.27, 2.73 and 3.33%, respectively) can be obtained.

Improving short-term water demand forecasting using evolutionary algorithms

Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.

A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?

The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of technological developments, transforming data acquisition and treatment.

Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution

Short-term water demand forecast is one of the most important technology for urban water supply management. The accuracy and timeliness of the forecast have an important impact. Most of the reported water demand forecast models based on deep learning methods apply a manual features extraction strategy, resulting in incomplete mining of the data and weak model self-adaptability capability. To address these issues, a new framework of short-term water demand forecast is proposed, in which a data preprocessing approach, S-H-ESD (Seasonal Hybrid Extreme Student Deviate), and a forecasting model, Conv1D-GRU (one-dimensional convolution-gated recurrent unit) are mainly developed. Based on the historical monitoring data, different hyper-parameter settings and training strategies were carried out with the proposed models. The results show that the data preprocessing model S-H-ESD can effectively deal with a variety of abnormal values, therefore significantly improving the accuracy of forecast (When the training dataset length is 7 days, the average accuracy of the three models is improved by1.23% when using S-H-ESD method compared with Z-Score method) and the Conv1D-GRU model shows better capability in forecast accuracy and self-adaptability of data features extraction compared with other models in literature (GRUN, ANN). With the achieving optimal parameter setting and training strategy, the developed methodology shows the best forecasted value of MAPE and NSE indicator are 1.677%, 0.983, respectively.

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

A novel deep neural network architecture for real-time water demand forecasting

Short-term water demand forecasting (StWDF) is the foundation stone in the derivation of an optimal plan for controlling water supply systems. Deep learning (DL) approaches provide the most accurate solutions for this purpose. However, they suffer from complexity problem due to the massive number of parameters, in addition to the high forecasting error at the extreme points. In this work, an effective method to alleviate the error at these points is proposed. It is based on extending the data by inserting virtual data within the actual data to relieve the nonlinearity around them. To our knowledge, this is the first work that considers the problem related to the extreme points. Moreover, the water demand forecasting model proposed in this work is a novel DL model with relatively low complexity. The basic model uses the gated recurrent unit (GRU) to handle the sequential relationship in the historical demand data, while an unsupervised classification method, k-means, is introduced for the creation of new features to enhance the prediction accuracy with less number of parameters. Real data obtained from two different water plants in China are used to train and verify the model proposed. The prediction results and the comparison with the state-of-the-art illustrate that the method proposed reduces the complexity of the model six times of what achieved in the literature while conserving the same accuracy. Furthermore, it is found that extending the data set significantly reduces the error by about 30%. However, it increases the training time.

An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting

Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.

Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.

Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.

A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities

Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). The huge amount of data generated contain measurements such as flow or pressure. Applying suitable algorithms to these data can warn about the possibility of leakage within the distribution network as soon as the data are gathered. Currently, the algorithms that deal with this problem are the result of numerous short-term water demand forecasting (WDF) approaches. However, in general, these WDF approaches share two shortcomings. The first one is that they provide low-frequency predictions. That is, most of them only provide predictions with 1-hour time steps, and only a few provide predictions with 15 min time steps. The second one is that most of them require estimating the annual seasonality or taking into account not only data about water demand but also about other factors, such as weather data, that make their use more complicated. To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques. The approach has a twofold contribution. Firstly, the predictions are provided with 1 min time steps within a time lead of 24 hours. Secondly, the laborious estimation of annual seasonality or the addition of other factors, such as weather data, is not needed. The paper also presents the promising results obtained after applying the approach for water demand forecasting to a real project for the detection and location of water leakages.

Real-Time Dynamic Hydraulic Model of Water Distribution Networks

The impact of climate change and increasing urbanisation throughout the world has forced water utility managers to increase the efficiency of water resources. Reduction of real (or physical) water losses plays a crucial role in improving the efficiency of water supply systems. Considering these challenges, it will not be enough to rely only on traditional approaches to solve the problem of water losses. Therefore, more advanced techniques need to be developed and utilized. Recently, a framework for a real-time dynamic hydraulic model for potable water loss reduction was proposed. This paper focuses mainly on the three major components of the proposed real-time dynamic hydraulic model framework for potable water loss reduction, which have been developed recently. These are background leakage detection, pressure management, and water demand forecasting. A background leakage detection algorithm was proposed which, amongst others, permits the localisation of potential critical nodes or pipes with higher leakage flow in the network where such pressure management could be performed. More so, new controllers (algorithms) which perform pressure management by accurately setting the pressure, using either a pressure control valve or variable speed pump, have been constructed. In addition, background leakage flow is greatly affected by demand variations, a water demand forecasting model is constructed with the aim of annexing the demand variation for multi-period leakage analysis. Thus, a short-term water demand forecast utilising the Model Conditional Processor was constructed to forecast the following hour demand and the associated predictive uncertainty. Although each of these components have been tested independently, future work is ongoing for merging these components and integration within the dynamic hydraulic model framework.

A Comparison of Short-Term Water Demand Forecasting Models

This paper presents a comparison of different short-term water demand forecasting models. The comparison regards six models that differ in terms of: forecasting technique, type of forecast (deterministic or probabilistic) and the amount of data necessary for calibration. Specifically, the following are compared: a neural-network based model (ANN_WDF), a pattern-based model (Patt_WDF), two pattern-based models relying on the moving-window technique (αβ_WDF and Bakk_WDF), a probabilistic Markov chain-based model (HMC_WDF) and a naive benchmark model. The comparison is made by applying the models to seven real-life cases, making reference to the water demands observed over 2 years in district-metered areas/water distribution networks of different sizes serving a different number and type of users. The models are applied in order to forecast the hourly water demands over a 24-h time horizon. The comparison shows that a) models based on different techniques provide comparable, medium-high forecasting accuracies, but also that b) short-term water demand forecasting models based on moving-window techniques are generally the most robust and easier to set up and parameterize.

Short-term forecasting of hourly water demands - a Portuguese case study

Predicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naive models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances.

Short-term forecasting of hourly water demands - a Portuguese case study

Predicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naïve models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances.

Short-Term Water Demand Forecast Based on Deep Learning Method

AbstractShort-time water demand forecasting is essential for optimal control in a water distribution system (WDS). Current methods (e.g., time-series models and conventional artificial neural netwo...