Water Demand Forecasting Research Articles

An applied study of a technique incorporating machine learning algorithms to optimize water demand prediction

Abstract In water resource management, accurate water demand prediction is essential for developing effective water supply strategies and optimizing resource allocation. This study aims to investigate machine learning algorithms, particularly echo state network (ESN) models, to improve the accuracy and efficiency of water demand prediction. ESN models are selected for their excellent nonlinear time series processing capabilities, which address the challenges of traditional prediction methods when dealing with complex water resource systems. By optimizing the parameters of the ESN model, the study hopes to provide a more scientific and efficient method for residential domestic and agricultural water demand forecasting, thus supporting more refined water resources planning and management decisions. Residential water demand prediction and crop water demand prediction are the two parts of this study. In the prediction of residential water demand, based on the actual data of City Z, the optimized ESN model predicts the water demand in 2025, and the total water demand in the baseline scenario is 790.9 million m3, and the expected values of water demand in different scenarios combined with the economic growth rate and the change of water price range from 659.4708 million m3 to 730.448 million m3. The article’s accuracy analysis of crop water demand prediction indicates that the model’s relative errors in predicting the water demand of the three major crops are limited to 10%. The ESN model optimized using the machine learning algorithm in this paper has good potential for water demand prediction and is an efficient and accurate prediction tool for managing water resources.

Performance of TANN, NARX, and GMDHT Models for Urban Water Demand Forecasting: A Case Study in a Residential Complex in Qom, Iran

To keep the balance between demand and supply, methods based on the average per capita consumption were usually applied to predict water demand. More complicated models such as linear regression and time series models were developed for this purpose. However, after the introduction of artificial neural networks (ANNs), different applications of this method were used in the field of water supply management, especially for urban water demand prediction. In this study, multiple types of ANNs were studied to understand their suitability for a residential complex water demand prediction in the city of Qom, Iran. The results indicated that time series ANN (TANN), nonlinear autoregressive network with exogenous inputs (NARX), group method of data handling time series (GMDHT), and their wavelet counterparts (i.e., w-TANN and w-NARX) exhibited varying degrees of performance. Among the aforementioned models, w-NARX performed the best (based on the average overall error) with the test set root mean squared error (MSE) of 49.5 (m3 /h) and R of 0.93, followed by the GMDHT model with the test set MSE of 104 (m3 /h) and R of 0.97 and w-TANN with the test set MSE of 68.8 (m3 /h) and R of 0.91. In addition, the feedback connection in NARX compared to TANN demonstrated overall performance improvement.

Forecasting Urban Peak Water Demand Based on Climate Indices and Demographic Trends

Austria’s water utilities are facing new challenges due to advancing climate change. In recent years, changes in water demand have been observed. Water demand forecast models are required to assess these changes and react to them in a sustainable way. In this study, an existing modeling approach was extended with new climate indices. The multiple linear regression model was applied to different study sites. The model was trained with a training dataset and validated with a test dataset. The performance of the model was assessed using common parameters, such as the mean absolute percentage error. In a further step, the modeling approach was applied to climate projections to estimate the change in water demand for three different representative concentration pathways (RCPs). The change in water demand due to population growth was then considered and combined with the change due to climate change. RCP2.6 shows an average 14% increase in water demand for the period 2051–2070, with climate change (average increase of 0.7%) playing a negligible role. For RCP4.5, an increase of 16% is predicted, while the highest increase of 19% is observed in RCP8.5. Population growth is responsible for most of the increase.

Correction: Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes

Correction: Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches to water resource management (WRM) struggle with real-time data acquisition, effective data analysis, and intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) and Big Data Analytics (BDA) are at the forefront and have the potential to revolutionize the way water resources are managed. This paper reviews the current applications of AI and BDA in WRM, highlighting their capacity to overcome existing limitations. It includes the investigation of AI technologies, such as machine learning and deep learning, and their diverse applications to water quality monitoring, water allocation, and water demand forecasting. In addition, the review explores the role of BDA in the management of water resources, elaborating on the various data sources that can be used, such as remote sensing, IoT devices, and social media. In conclusion, the study synthesizes key insights and outlines prospective directions for leveraging AI and BDA for optimal water resource allocation.

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 urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes

Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes

Dynamic Graph Convolution-Based Spatio-Temporal Feature Network for Urban Water Demand Forecasting

Urban water demand forecasting is the key component of smart water, which plays an important role in building a smart city. Although various methods have been proposed to improve forecast accuracy, most of these methods lack the ability to model spatio-temporal correlations. When dealing with the rich water demand monitoring data currently, it is difficult to achieve the desired prediction results. To address this issue from the perspective of improving the ability to extract temporal and spatial features, we propose a dynamic graph convolution-based spatio-temporal feature network (DG-STFN) model. Our model contains two major components, one is the dynamic graph generation module, which builds the dynamic graph structure based on the attention mechanism, and the other is the spatio-temporal feature block, which extracts the spatial and temporal features through graph convolution and conventional convolution. Based on the Shenzhen urban water supply dataset, five models SARIMAX, LSTM, STGCN, DCRNN, and ASTGCN are used to compare with DG-STFN proposed. The results show that DG-STFN outperforms the other models.

Regional water demand forecasting based on shared socio-economic pathways in the Zhanghe River Basin

Abstract Based on the framework of shared socio-economic pathways, this study predicts future population and economic conditions of the Zhanghe River Basin and combines with the water quota to predict future water demand. First, the localization parameter system is constructed. Furthermore, the water demand is calculated. The results show that (1) under regional competitive pathway, the population is the largest, while under uneven pathway, the population is the smallest. The largest economic forecast is obtained under fossil fuel development pathway, while the smallest economic forecast is obtained under regional competitive pathway. (2) The results for domestic and economic water use in the basin show that the annual water demand shows an increasing trend. Fossil fuel development pathway is the scenario with the highest socio-economic water demand, while regional competitive pathway is the scenario with the least. (3) The Zhanghe River Basin faces a high risk of water resource shortage in the future. Even under the situation of minimum socio-economic water demand, the total water demand is difficult to meet fully. The forecasting framework established in this paper has high application value and can provide a reference for water demand forecasting and prospective water demand management in river basins.

Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia

Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia

A Novel Model Based on Deep Learning Approach Combining Data Decomposition Technique and Grouping Distribution Strategy for Water Demand Forecasting of Urban Users

Urban user water demand prediction (WDP) is of significant importance for smart water supply system, which can provide a strong decision-making basis for the dispatching and management of smart water supply system. However, owing to the fluctuation, intermittence and nonstationarity of the user’s water consumption in urban buildings, it is extremely difficult to predict accurately. Therefore, a novel short-term WDP model (Singular Spectrum Analysis Convolutional Neural Network Bidirectional Gate Recurrent Unit, SSA-CNN-BiGRU) is proposed to promote the stability and accuracy of WDP, which successfully introduces organic combinations including deep learning, decomposition technique, and data partitioning policies into the domain of WDP. First, raw data are decomposed into components that carry distinct frequency signals for weakening its nonstationarity and complexity. Then, all the components are automatically divided into several groups using clustering algorithm based on their entropy, after which deep learning method is adopted to predict by groups. Finally, the predicted result of each group is summed up to be fused as the final value. To validate the predictive performance of SSA-CNN-BiGRU, real data have been selected for this study. In experiments, SSA-CNN-BiGRU achieved a fitting of 94.73%. Comparison by relevant evaluation metrics demonstrates that the proposed model exhibits superior performance, thus providing a more accurate basis for WDP.

Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study

Accurate water demand forecasting is essential for water utilities for effective water planning and pump scheduling. To conduct effective water planning and pump scheduling, Sydney Water needs a reliable method for predicting water demand. Short-term water demand forecasting deep learning- based models have been applied to different datasets collected from various water plants in the Greater Sydney region. This paper uses correlation analysis, the attention mechanism and a deep convolutional neural network to identify and study the importance of n number of factors that inform water prediction in the Greater Sydney region. The aim of this study is to develop, evaluate and compare multi- variate input deep learning-based models to predict water demand in various locations in the Greater Sydney region. This paper investigates five models — the recurrent neural network (RNN), long short- term memory networks (LSTM), bidirectional long short-term memory networks (BiLSTM), gated recurrent unit (GRU), and the deep convolutional neural network (CNN). The results of this study demonstrate that all the proposed models achieved a superior forecasting performance with accuracy hits of 97% using water consumption and climate factors as input data.

New memory-based hybrid model for middle-term water demand forecasting in irrigated areas

The energy demand and their associated costs in pressurized irrigation networks together with water scarcity are currently causing serious challenges for irrigation district’s (ID) managers. Additionally, most of the new water distribution networks in IDs have been designed to be operated on-demand complexing ID managers the daily decision-making process. The knowledge of the water demand several days in advance would facilitate the management of the system and would help to optimize the water use and energy costs. For an efficient management and optimization of the water-energy nexus in IDs, longer term forecasting models are needed. In this work, a new hybrid model (called LSTMHybrid) combining Fuzzy Logic (FL), Genetic Algorithm (GA), LSTM encoder-decoder and dense or full connected neural networks (DNN) for the one-week forecasting of irrigation water demand at ID scale has been developed. LSTMHybrid was developed in Python and applied to a real ID. The optimal input variables for LSTMHydrid were mean temperature (°C), reference evapotranspiration (mm), solar radiation (MJ m−2) and irrigation water demand of the ID (m3) from 1 to 7 days prior to the first day of prediction. The optimal LSTMHybrid model selected consisted of 50 LSTM cells in the encoder submodel, 409 LSTM cells in the decoder submodel and three hidden layers in the DNN submodel with 31, 96 and 128 neurons in each hidden layer, respectively. Thus, LSTMHybrid had a total of 1.5 million parameters, obtaining a representativeness higher than 94 % and an accuracy around of 20 %.

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. The closely-intertwined ecosystem of water and power infrastructures is commonly known as water-energy nexus. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a Boolean solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

Analysis of driving factors of water demand based on explainable artificial intelligence

Study regionBeijing–Tianjin–Hebei Region, China Study focusUnderstanding factors driving water demand is crucial for water resource planning and management. However, traditional models fail to capture the complex nonlinear factors that drive real-world water demand. While machine learning models can capture nonlinear relationships, comprehending the complex mechanisms underpinning the models is difficult. Therefore, we combined machine learning with explainable artificial intelligence to analyze the factors driving water demand in the study region. New hydrological insights for the regionA water demand forecasting framework is proposed for analyzing the factors driving water demand. Results show that the main driving factors differ across city types. Population is the most crucial factor influencing water demand, with an effect size of 50.30%, 39.72%, and 31.79% in service-based, industrial, and agricultural cities, respectively. The second- and third-most important factors in service-based cities are the added value of secondary industry (AVSI) and irrigated area (IA), respectively. In industrial and agricultural cities, the second- and third-most-important factors are AVSI and temperature and temperature and IA, respectively. By quantifying the nonlinear relationships between water demand and driving factors, we identify the critical points associated with changes in correlation structure, such as urbanization rate (70%) and per capita disposable income (25,000 CNY per annum). Thus, this study can serve as a valuable reference for developing accurate models to forecast water demand.

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.

Assessment of ERA5-Land Data in Medium-Term Drinking Water Demand Modelling with Deep Learning

Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This work proposes to analyse the suitability of ERA5-Land reanalysis data as weather input in water demand modelling. A multivariate deep learning model based on the long short-term memory architecture is used in this study over a prediction horizon ranging from seven days to two months. The performance of the model, fed by ground station data and ERA5-Land data, is compared and analysed. Close-to-operative forecasting is then presented using observed data for training and ERA5-Land dataset for testing. The results highlight the reliability of the proposed architecture fed by ERA5-Land data for different time horizons. In particular, the ERA5-Land shows promising performance as input of the multivariate machine learning forecasting model, although some meteorological biases are present, which can be improved, especially in close-to-operative application with bias correction techniques. The proposed study leads to practical implications in the use of regional climate model outputs to support drinking water forecasting for sustainable and efficient management of water distribution systems.

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.

Water demand forecast: a literature review

Forecasting water demand is fundamental to a region's social and economic development. In the literature there are several studies with specific applications, However, the topic still lacks a comprehensive view. Therefore, this article proposes a integrative review of the literature, to obtain an overview of the subject (methods, areas of application, objectives, and other factors). Using Methodi Ordinatio methodology, 74 articles with scientific relevance for analysis were selected, most of them published in the USA, Australia, and the United Kingdom. It was concluded that in the use of methods there is a predominance of the approach of artificial neural networks and regression analyzes. As for the application, most studies were for forecasting residential demand.