Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani, Sina; Candelieri, Antonio; Archetti, Francesco; Naser, Gholamreza

doi:10.3390/w10020142

Cited by 26 publications

(17 citation statements)

References 36 publications

(34 reference statements)

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“…In a recent study, Shabani et al. (2018) proposed a hybrid model which comprises a GEP-supervised and K-means clustering (unsupervised) learning process for short-term water demand forecasting.…”

Section: Main Textmentioning

confidence: 99%

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Oyebode

Babatunde

Monyei

et al. 2019

Heliyon

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The purpose of this review is to establish and classify the diverse ways in which evolutionary computation (EC) techniques have been employed in water demand modelling and to identify important research challenges and future directions. This review also investigates the potentials of conventional EC techniques in influencing water demand management policies beyond an advisory role while recommending strategies for their use by policy-makers with the sustainable development goals (SDGs) in perspective. This review ultimately proposes a novel integrated water demand and management modelling framework (IWDMMF) that enables water policy-makers to assess the wider impact of water demand management decisions through the principles of egalitarianism, utilitarianism, libertarianism and sufficientarianism. This is necessary to ensure that water policy decisions incorporate equity and justice.

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Section: Main Textmentioning

confidence: 99%

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Oyebode

Babatunde

Monyei

et al. 2019

Heliyon

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“…Moreover, GEP presents mathematical equations that clarify the relationship between input and output variables by a factor of 100-10000 [63,79,80]. The superiority of GEP and the advantages of this technique interested researchers to develop more sophisticated models with hybrid methods such as combining the extended Kalman filter [81], clustering the consumption values [82], Wavelet decomposition [39], and phase space reconstructed GEP (PSR-GEP) [42] in forecasting urban drinking water consumption. The results showed that GEP models are highly sensitive to wavelet decomposition when attempting to improve the performance of the models.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

Yousefi

Courtice

Naser

et al. 2020

Water

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This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.

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“…Precise short-term prediction of urban water demand provides guidance for the planning and management of water resources and plays an important role in the economic operation of a water supply system. Therefore, various water demand prediction models, such as support vector regression (SVR) [1,2], random forests regression [3], artificial neural network (ANN) [4], Markov chain model [5], and hybrid models [6][7][8][9], have been widely developed in the past few decades. Research regarding water demand prediction generally focuses on methods involving ANN, which are nonparametric data-driven approaches applicable for building nonlinear mapping from input to output variables for estimating nonlinear continuous functions with an arbitrary accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Zhang

Long

et al. 2019

Water

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Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBESN model is tested using historical hourly water demand data obtained from an urban waterworks in Zhuzhou, China. The performance of the proposed model is compared with those of ESN, continuous deep belief neural network, and support vector regression models. The correlation coefficient (r2), normalized root-mean-square error (NRMSE), and mean absolute percentage error (MAPE) are adopted as assessment criteria. Forecasting results obtained in the testing stage indicate that the CDBESN model has the largest r2 value of 0.995912 and the smallest NRMSE and MAPE values of 0.027163 and 2.469419, respectively. The prediction accuracy of the proposed model clearly outperforms those of the models it is compared with due to the good feature extraction ability of CDBN and the excellent feature learning ability of ESN.

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Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Cited by 26 publications

References 36 publications

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

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