Water Demand Forecasting Research Articles

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Water Demand Forecasting in Multiple District Metered Areas Based on a Multi-Scale Correction Module Neural Network Architecture

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5%-20% per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

Analysis and forecast of crop water demand in irrigation districts across the eastern part of the Ebro river basin (Catalonia, Spain): estimation of evapotranspiration through copernicus-based inputs

AbstractThe agricultural sector is currently facing the uncertainty that accompanies climate change in terms of the availability of water resources, as well as the need to balance the water demand for agricultural irrigation with other uses in river basins. In Spain, irrigation districts (IDs) play a very important role in the management of water resources. The efficiency of ID water management involves finding an equilibrium between supply and demand. It is in relation to the latter where the uncertainty is greatest, because until now no tools have been available to characterize water demands with sufficient precision throughout irrigation campaigns. ID managers need precise information and the development of tools to support decision making in planning and water management. Therefore, this study aims to identify, compare and analyse the differences between the demands, allocations and consumptions of water for irrigation in different IDs of the eastern part of the Ebro basin during six consecutive growing seasons. In addition, projections of water demands up to 2100 are conducted using a dataset of six global climate models under different climate scenarios. Novel advances in remote sensing for evapotranspiration approaches using Copernicus-based inputs were used in this study. Large variabilities in water demands among IDs and in the adjustments between demands and allocations were observed, suggesting there is still much room for the improvement of water management. All climate projections have a very clear pattern indicating an upward trend in water demands until the end of the century.

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

Abstract 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.

Application of Network Reconstruction Algorithm to Compute Maximum Flow for Water Supply Network: A Case Study of the City of Bulawayo, Zimbabwe

Determining the maximum flow value in Water Supply Networks (WSN) is a common problem that is being faced by many cities during and after designing of WSN. In this article, the network reconstruction (NR) algorithm is applied to compute the maximum flow value for the city’s WSN based on the actual data. The city of Bulawayo has been selected for the following reasons, availability of research data, the city is facing severe water shedding and several studies have focused only on other issues such as alternative water sources, leakage detection and demand forecasting. The goal of this study is to determine the maximum flow value from the sources to the reservoirs. The results have revealed that the computed maximum flow value is within the estimated range of 110,000 cubic meters to 190,000 cubic meters. Dam sensitivity analysis were considered to determine the sources to give maintenance priority before the rain season. Several recommendations were suggested to improve the water supply situation.

A large-scale evaluation of machine learning algorithms in mid-term water demand forecasting

ABSTRACT Water utilities currently face challenges in accurately measuring user-end consumption as they have to visit every conventional meter that is installed in their service area. This study explores the use of accessible machine learning models to forecast water demand at two crucial temporal scales, addressing practical needs such as customer billing. To draw meaningful conclusions, these models were tested and trained from actual user-end water consumption data from over 2.1 million customers over a 10-year period. These data were provided by the Water and Sewage Company of Greece (EYDAP). The large-scale experiment included the use of statistical models (ARIMA and SARIMA), deep learning [long short-term memory (LSTM)], and clustering techniques (k-NN algorithm). The model with the best performance was ARIMA, outperforming all the other models including the more complex ones. The LSTM model did not perform as expected in predicting water consumption, it excelled only in predicting the total amount of water consumed. These forecasts can be valuable tools for water utility companies, aiding in tasks such as customer billing and water balance calculations. This paper covers all the necessary steps that must be taken to achieve meaningful user-end water consumption forecasts, from raw data preprocessing to hyperparameter tuning for machine learning algorithms.

A Water Demand Forecasting Model Based on Generative Adversarial Networks and Multivariate Feature Fusion

Accurate long-term water demand forecasting is beneficial to the sustainable development and management of cities. However, the randomness and nonlinear nature of water demand bring great challenges to accurate long-term water demand forecasting. For accurate long-term water demand forecasting, the models currently in use demand the input of extensive datasets, leading to increased costs for data gathering and higher barriers to entry for predictive projects. This situation underscores the pressing need for an effective forecasting method that can operate with a smaller dataset, making long-term water demand predictions more feasible and economically sensible. This study proposes a framework to delineate and analyze long-term water demand patterns. A forecasting model based on generative adversarial networks and multivariate feature fusion (the water demand forecast-mixer, WDF-mixer) is designed to generate synthetic data, and a gradient constraint is introduced to overcome the problem of overfitting. A multi-feature fusion method based on temporal and channel features is then derived, where a multi-layer perceptron is used to capture temporal dependencies and non-negative matrix decomposition is applied to obtain channel dependencies. After that, an attention layer receives all those features associated with the water demand forecasting, guiding the model to focus on important features and representing correlations across them. Finally, a fully connected network is constructed to improve the modeling efficiency and output the forecasting results. This approach was applied to real-world datasets. Our experimental results on four water demand datasets show that the proposed WDF-mixer model can achieve high forecasting accuracy and robustness. In comparison to the suboptimal models, the method introduced in this study demonstrated a notable enhancement, with a 62.61% reduction in the MSE, a 46.85% decrease in the MAE, and a 69.15% improve in the R2 score. This research could support decision makers in reducing uncertainty and increasing the quality of water resource planning and management.

Knowledge-based Bi-correction model for achieving effective lag-free characteristic on daily urban water demand forecasting

Accurate demand forecasting is crucial for the efficient management of smart cities. However, the non-stationary and nonlinear characteristics of daily water demand series present significant challenges, leading to the lag forecasting problem and unreliable predictions. To address these challenges, this paper introduces a novel bi-correction model based on knowledge extraction, named the knowledge-based bi-correction model (KbBcM). The KbBcM leverages prior and posterior knowledge to achieve effective lag-free forecasting. The prior knowledge, in the form of sequence and feature information, is extracted using an autoregressive long short-term memory (AR-LSTM) network and a newly proposed lag penalty attention (LPA) module. Furthermore, a hybrid stacked LSTM network is employed to obtain primary forecasting results, which are then corrected with a masked weighted Markov chain (MWMC) based on posterior knowledge. Addressing the limitations of current metrics focused on absolute error, we also introduce the novel mean prediction trend effectiveness (MPTE) to comprehensively evaluate the effectiveness of lag-free forecasting performance. Comparation experiments performed on the urban water demand dataset of a megacity demonstrate the superiority of the KbBcM over baseline models. Additionally, ablation studies are performed to examine the effectiveness of the proposed sub-modules, further validating the robustness and reliability of the KbBcM in addressing the complexities of urban water demand forecasting.

Reliable multi-horizon water demand forecasting model: A temporal deep learning approach

Accurate water demand forecasting can help understand water usage dynamics, which has a potential application in water saving and demand management. Despite extensive research, most exiting methods cannot capture long-range dependency of water demand and maintain high-accuracy in multi-horizon forecasting continuously. To address these issues, the focus of this research is primarily on the temporal model for multi-horizon water demand forecasting using deep learning. This research proposes a signal enhancement strategy and develops an attention-based deep learning model. Firstly, the Fourier transform is used for sparse approximation of water demand data, which helps represent the signal more compactly. To enhance the performance of multi-horizon forecasting model, the complex water demand time series is decomposed into trend and seasonal components. Then, an attention mechanism is utilized to learn temporal dependencies within the data, providing assists for multi-horizon water demand forecasting. Furthermore, a comparative study is carried out between the proposed model and the state-of-the-art methods using the water demand data from four usage scenarios. Experimental results suggest that the present model has the ability to produce accurate and robust forecasting, especially for multi-horizon water demand. Therefore, this research has the potential to enhance water resource management in a socioecological context.

Review of the Mechanism and Methodology of Water Demand Forecasting in the Socio-Economic System

As global water scarcity becomes increasingly acute, water demand forecasting has emerged as a critical component in water resource management and planning. This review aims to comprehensively survey and analyze the current state of research, existing issues, and development trends in the field of water demand forecasting. Presently, there are numerous studies on water demand forecasting; however, most of the forecasting results tend to be overestimated. On the mechanistic level, research has gradually shifted from considering single factors to accounting for the complex influences of multiple factors. This paper summarizes the mechanism of water demand from the three levels of agriculture, industry, and residential life. In terms of forecasting methods, various techniques have been explored and applied, particularly new methods based on artificial intelligence and machine learning, which have demonstrated significant advantages in improving forecasting accuracy and handling nonlinear relationships. Despite the notable progress and practical achievements in water demand forecasting, several challenges and issues remain. Future research should focus on diversifying methodologies, comprehensively considering multiple influencing factors, further refining forecasting models and technical systems, strengthening uncertainty and risk management, and emphasizing practical applications and policy guidance.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

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.

Ensemble Empirical Mode Decomposition Granger Causality Test Dynamic Graph Attention Transformer Network: Integrating Transformer and Graph Neural Network Models for Multi-Sensor Cross-Temporal Granularity Water Demand Forecasting

Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD–Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems.

Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China

Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making.

Forecasting Water Demand With the Long Short-Term Memory Deep Learning Mode

Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining ARIMA's proficiency in linear trend and seasonal modeling with LSTM's strength in capturing nonlinear time dependencies. In these experiments, the authors trained and evaluated using daily water demand data from 2015 to 2020, with its performance validated for the year 2021. The ARIMA-LSTM model demonstrates promising results, outperforming individual models in terms of accuracy. In validation, it achieves a high coefficient of determination (R2) of 0.98 and a significantly low root mean square error (RMSE) of 2.94. These metrics indicate an excellent fit to the data and a high level of precision in its predictions. The significance of this research lies in its potential to advance the field of urban water demand forecasting, ultimately contributing to better water resource management and sustainability in urban areas.

Modeling and Forecasting of Water Demand in the City of Istanbul Using Artificial Neural Networks Optimized with Rao Algorithms

In this study, a hybrid artificial neural network (ANN)-Rao series (Rao_1, Rao_2, and Rao_3) algorithm model was developed to analyze water consumption in Istanbul province, Turkey. A multiple linear regression (MLR) model was developed and an ANN was also trained with back-propagation (BP) artificial bee colony (ABC) algorithms for comparison. Gross domestic product and population data were treated as independent variables. To test the accuracy of the presently developed hybrid model, its outputs were compared with those of ANN-BP, ANN-ABC, and MLR models. Error values calculated for the test set indicated that the ANN-Rao_3 algorithm outperformed the MLR, ANN-BP, and ANN-ABC reference models as well as ANN-Rao_1 and ANN-Rao_2 algorithms. Therefore, using the ANN-Rao_3 model, water consumption forecasts for Istanbul province were generated out to 2035 for low-, expected-, and high-water demand conditions. The model-generated forecasts indicate that the water requirements of Istanbul in 2035 will be between 1182.95 and 1399.54 million m3, with the upper-range estimates outpacing supplies. According to low and expected scenarios, there will be no problem in providing the water needs of Istanbul until 2035. However, according to high scenario, water needs of Istanbul will not be provided as of 2033.Therefore, water conservation policies should be enacted to ensure provision of the water needs of Istanbul province from 2033 onward.

Multi-objective optimization operation of multiple water sources under inflow-water demand forecast dual uncertainties

Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part.

Improving urban water demand forecast using conformal prediction-based hybrid machine learning models

This paper presents a probabilistic forecasting method that predicts the future water consumption patterns, taking into account the inherent uncertainty in the system. The proposed method leverages statistical techniques to estimate the probability distribution of future water demand scenarios. The findings of this study will provide decision-makers with a range of possible alternatives that facilitate better planning and management of water resources. We developed a conformal prediction-based hybrid demand forecast model, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) while comparing it with other machine learning models for probabilistic hourly water demand forecasting. Additionally, we address crucial considerations when implementing a probabilistic forecasting system, including selecting appropriate data and choosing model parameters. The performance of the proposed model is validated for probabilistic water demand forecasting in real-world settings. Results indicate noteworthy improvement in deterministic and probabilistic predictions by 10 % and 26.7 %, respectively. The findings underscore the potential benefits of this approach for improved decision-making and resource management. The results also demonstrate that the proposed hybrid model outperforms the compared machine learning models with better prediction performance and increased robustness for handling prediction uncertainties.