Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand

Carvalho, Taís Maria Nunes; Filho, Francisco de Assis de Souza

doi:10.1007/s11269-021-02902-7

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…Given the pivotal role of input feature preparation in establishing a dependable water demand forecasting model, the selection of the feature matrix must be carried out with precision. For short-term water demand predictions, often involving intervals as frequent as hours or minutes, input selection typically encompasses previous water demand data and climatic conditions [30]. The multitude of factors influencing water demand complicate the forecasting task.…”

Section: Feature Extraction and Sample Processingmentioning

confidence: 99%

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

Shan,

Ni,

Chen

et al. 2023

Water

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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.

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Section: Feature Extraction and Sample Processingmentioning

confidence: 99%

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

Shan,

Ni,

Chen

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…Since preparing the input feature is paramount for building a reliable water demand forecasting model, the feature matrix must be carefully selected. For short-term water demand forecasts (such as hourly or minute-level intervals), input selection usually takes previous water demand data and climate conditions into account(Nunes Carvalho and Filho, 2021). Many factors have impact on the water demand, which increase the difficulty of forecasting.…”

Section: Feature Extraction and Sample Processingmentioning

confidence: 99%

A Deep Learning Model with Attention-BiLSTM Networks Combining XGBoost Residual Correction for Short-Term Water Demand Forecast

Shan

Wang³

et al. 2022

Preprint

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As a key component of water distribution management, reliable short-term water demand forecasting plays a fundamental role in the optimal control for water supply. Most reported approaches based on deep learning omit the two-way information flow existed in historical water demand data and the model inputs cannot automatically highlight the significance of crucial features to current water demands, which could have impact on the prediction accuracy. Owing to the high nonlinear changes and fluctuations in water demand series, making accurate forecast a challenging task. To address the problem in this study, maximal information coefficient (MIC) is presented for feature extraction analysis, deep learning with Attention-BiLSTM networks is developed, to reinforce the performance, that combining the XGBoost algorithm as a residual correction module to forecast short-term water demand. Hyper-parameter configurations are conducted with the models. Finally, the superiority of proposed method is illustrated by comparing to other benchmark models. The results show that the proposed method outperforms other predictive models, in which both on the accuracy and stability.

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Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization

Kilinç

Ahmadianfar²,

Demir

et al. 2023

Water Resour Manage

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Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand

Cited by 10 publications

References 31 publications

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

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

A Deep Learning Model with Attention-BiLSTM Networks Combining XGBoost Residual Correction for Short-Term Water Demand Forecast

Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization

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