Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Shuang, Qing; Zhao, Rui

doi:10.3390/w13030310

Cited by 18 publications

(4 citation statements)

References 59 publications

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“…LSTM achieved an RMSE value of 0.13, and BPNN achieved an RMSE value of 0.48. Shuang and Zhao [21] used the Beijing-Tianjin-Hebei region annual water report data to predict water demand for a particular region. They used eleven statistical-based and machine-learning-based models to conduct the study.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Rustam

Ishaq

Kokab

et al. 2022

Water

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Rustam

Ishaq

Kokab

et al. 2022

Water

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“…Within this class of models, gradient boosting decision trees (GBDT) have recently outperformed traditional methods like RF in various regression and classification tasks [20]. For instance, in their comparative study of single and ensemble statistical and ML forecasting models for UWD, Shuang and Zhao [21] demonstrated that GBDT outperforms RF and adaptive boosting (AdaBoost) in terms of accuracy and robustness. The application of GBDT to relatively small datasets allows for reasonable tuning and training times.…”

Section: Introductionmentioning

confidence: 99%

Combining wavelet-enhanced feature selection and deep learning techniques for multi-step forecasting of urban water demand

Hao,

Cominola,

Castelletti

2024

Environ. Res.: Infrastruct. Sustain.

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Urban water demand (UWD) forecasting is essential for water supply network optimization and management, both in business-as-usual scenarios, as well as under external climate and socio-economic stressors. Different machine learning and deep learning models have shown promising forecasting skills in various areas of application. However, their potential to forecast multi-step ahead UWD has not been fully explored. Modelling uncertain UWD patterns and accounting for variations in water demand behaviors require techniques that can extract time-varying information and multi-scale changes. In this research, we comparatively investigate different state-of-the-art machine learning- and deep learning-based predictive models on 1-day- and 7-day-ahead UWD forecasting, using daily demand data from the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different machine learning and deep learning models on single- and multi-step daily UWD forecasting. These models include Artificial Neural Network (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory network with and without an attention mechanism (LSTM and AM-LSTM). We benchmark their prediction accuracy against autoregressive time series models. Second, we investigate the potential enhancement in predictive accuracy by incorporating the wavelet transform and feature selection performed by LightGBM into these models. Results show that, overall, wavelet-enhanced feature selection improves the model predictive performance. The hybrid model combining wavelet-enhanced feature selection via LightGBM with LSTM (WT-LightGBM-(AM)-LSTM) can achieve high levels of accuracy with Nash-Sutcliffe Efficiency larger than 0.95 and Kling–Gupta Efficiency higher than 0.93 for both 1-day- and 7-day-ahead UWD forecasts. Furthermore, performance is shown to be robust under the influence of external stressors causing sudden changes in UWD.

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“…Predicting water demand accurately using statistical and machine learning algorithms can aid managers in making informed decisions to prevent wastage and water scarcity. Shuang and Zhao (2021) In addition to predicting water demand, machine learning algorithms have been used to forecast water consumption at the household level. Duerr et al (2018) conducted short-term forecasts of water consumption in individual households using time-series methods.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of water subscribers by machine learning algorithms

Dahesh,

Tavakkoli‐Moghaddam,

Tajally

et al. 2023

Water & Environment J

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The problem of water scarcity and water crisis (e.g., stable water resources, reduced rainfall, increased urban population growth and lack of proper management of water consumption in urban and domestic water) has recently become a significant issue. Therefore, examining the behaviour of Tehran Province Water and Wastewater (TPWW) subscribers to identify high‐consumption subscribers and explain methods to encourage and educate them more about the correct water consumption pattern can help deal with this crisis. This study aims to use machine learning algorithms to build a robust model for the classification of subscribers in Tehran. Then, new subscribers can be classified into similar classes. For this reason, ensemble algorithms, support vector machines and neural networks are used to predict subscribers' behaviour. Then, the random forest algorithm from the set of ensemble algorithms is considered the best model for the TPWW case with 99% and 98% in train and test accuracy, respectively.

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Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Cited by 18 publications

References 59 publications

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

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

Combining wavelet-enhanced feature selection and deep learning techniques for multi-step forecasting of urban water demand

Classification of water subscribers by machine learning algorithms

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