Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

Sardinha-Lourenço, A.; Andrade‐Campos, A.; Antunes, André; Oliveira, Miguel

doi:10.1016/j.jhydrol.2018.01.047

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…In cases of high nonlinear and volatile time series, a forecasting model may not be able to fully capture and simulate the special characteristics, a fact that may lead to poor forecasting accuracy (Pradeepkumar and Ravi, 2017). Contemporary research has proposed some approaches to increase the forecasting performance (Sardinha-Lourenc ¸o et al, 2018). Clustering-based forecasting refers to the application of unsupervised machine learning in forecasting tasks.…”

Section: Clustering-based Forecasting 57mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Clustering-based Forecasting 57mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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show abstract

“…According to equation (12), the relative error value can be calculated by the values present in Table 6. Compared with the actual water consumption, the error of the calculations of the proposed method was less than 5%, and the average error was only 2.47%.…”

Section: Analysis and Discussion Of Calculation Resultsmentioning

confidence: 99%

“…At present, the application of artificial intelligence prediction methods to water consumption prediction mainly includes the following two ideas: a multiparameter prediction model and a time series-based model. Research on the prediction of drinking water demand in Portugal has shown that the univariate time series model based on historical data is useful and can be combined with other prediction methods to reduce errors [12]. e previously mentioned research has demonstrated that soft computing algorithms, such as ANNs, can better deal with the nonlinear problems in water resource demand management.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

Peng

Wang

et al. 2020

Advances in Civil Engineering

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The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

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“…Adamowski et al (2012) developed a forecast with an ANN based on temperature and precipitation data with relatively low errors; optimally performing SVM and ANN models produced a mean absolute relative error (MARE) of 5.47% and 2.95%, respectively. Sardinha-Lourenço et al (2018) evaluated the performance of a combined ANN-SVM method using ARIMA, ARIMA clustering, heuristic forecasting, mean weighting, mean squared error (MSE) weighting, and the parallel adaptive weighting strategy (PAWS). They reported R 2 values of 0.69 for the worst performing models to 0.97 for the best model, which produced a mean relative error (MRE) of 8.56%.…”

Section: Literature Reviewmentioning

confidence: 99%