Gene Expression Programing in Long Term Water Demand Forecasts Using Wavelet Decomposition

Yousefi, Peyman; Shabani, Sina; Mohammadi, Hadi; Naser, Gholamreza

doi:10.1016/j.proeng.2017.03.268

Cited by 16 publications

(12 citation statements)

References 19 publications

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“…Conventional regression models [3], autoregressive integrated moving average (ARIMA) [23], autoregressive integrated moving average with explanatory variable (ARIMAX) [24,25], artificial neural networks (ANN) [9,[26][27][28][29], a combination of conventional and ANN [11,12,30], feedforward neural networks [12,31], general regression neural networks [32,33], support vector machines [14,9,[34][35][36][37], gene expression programming [14,38], fuzzy regression [39], neuro-fuzzy systems [40,41], Fourier analysis [4], hybrid models (e.g. combined wavelet-ANN and wavelet-GEP) [13,38], fuzzy cognitive map learning method [42,43]. This research applies probabilistic ANN, GEP approach and a conventional method (MLR) to determine the performance of the methods with/without phase space reconstruction and wavelet decomposition in the case.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yousefi et al implemented sophisticated mathematical models to forecast water demand of City of Kelowna in monthly temporal scale. Their study assessed the performance of GEP using wavelet decomposition [38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, Labat notified about the improving ability of wavelet in models' performance [64]. Therefore, the application of wavelet brought researchers attention into the area such as denoising [65]; stream flow and water resources [66]; evaporation and climatic models [67]; groundwater level modeling [68]; water demand forecasting [13,38], where in most of the mentioned studies combination of Wavelet-ANNs performed accurately over conventional models without hybrid wavelet models (e.g. ARIMA, MLR, ANN and etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The arithmetic operations used in this study are {+, À, Â, x, x 2 , √x}, and GEP applies them to fit the best accuracy between input and output variables. Further details of GEP initial term values are in following of [14,38,59] to extract the GEP model for both 1-day delay and PSR. The results are shown in the Table 4 for the test period.…”

Section: Performance Of Gene Expression Programmingmentioning

confidence: 99%

See 3 more Smart Citations

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Yousefi¹,

Naser²,

Mohammadi³

2018

Wavelet Theory and Its Applications

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The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two preprocessing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multilinear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, autocorrelation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models' relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE) and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Performance Of Gene Expression Programmingmentioning

confidence: 99%

See 2 more Smart Citations

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Yousefi¹,

Naser²,

Mohammadi³

2018

Wavelet Theory and Its Applications

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“…2013, Cominola 2018. Because, temporal high-resolution time series enable better estimation of peak demands that are very important in a short-term plan along with to develop a maintaining plan for pipeline in long-term period to overcome to probabilistic failures in the network (Beal 2016, Shabani 2016, Yousefi 2017, Yousefi P. 2018. These theories also have interpolation and extrapolation methods based on recorded values in SCADA.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

Yousefi¹,

Naser²,

Mohammadi³

EPiC Series in Engineering

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A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

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A Hybrid Logistic Regression: Gene Expression Programming Model and Its Application to Mineral Prospectivity Mapping

et al. 2021

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Gene Expression Programing in Long Term Water Demand Forecasts Using Wavelet Decomposition

Cited by 16 publications

References 19 publications

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

A Hybrid Logistic Regression: Gene Expression Programming Model and Its Application to Mineral Prospectivity Mapping

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