Pareto-optimal MPSA-MGGP: A new gene-annealing model for monthly rainfall forecasting

Mehr, Ali Danandeh; Jabarnejad, Masood; Nourani, Vahid

doi:10.1016/j.jhydrol.2019.02.003

Cited by 32 publications

(7 citation statements)

References 39 publications

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“…Verification of a hydrological model is of importance to verify its reliability. To this end, Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), relative peak error (PE), and persistence index (PI) measures are used in this study as suggested in other hydrological studies (e.g., Khosravi et al, 2018;Yaseen et al, 2018;Danandeh Mehr et al, 2019). The NSE as a normalized statistic shows the prediction error relative magnitude in contrast with observed data variance.…”

Section: Performance Indicesmentioning

confidence: 99%

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari

Arashloo

Mehr

2020

Journal of Hydrology

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Section: Performance Indicesmentioning

confidence: 99%

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari

Arashloo

Mehr

2020

Journal of Hydrology

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“…For more than a couple of decades, GP has been one of the most robust ML techniques to solve symbolic regression problems [20,21]. However, it was also used to solve binary or multiclass classification problems [22,23]. The main reason for its great success over time is it's explicitly and ability to evolve mathematical expressions that can be easily analyzed, validated and applied in practice.…”

Section: Gp-based Modellingmentioning

confidence: 99%

Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

Mehr

Bagheri

Safari

2020

Gazi University Journal of Science

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Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper, for the first time, compares the efficiency of the decision tree (DT) and classic genetic programming (GP) data mining models developed for electrical energy demand forecasting in Nicosia, Northern Cyprus. The models were trained and tested using daily electricity consumptions measured during the period 2011-2016 and were compared in terms of three statistical performance indices including coefficient of determination, mean absolute percentage error and concordance coefficient. The prediction results showed that the proposed models can be effectively applied to forecasts of electrical energy demand. The results also indicated that the GP is slightly superior to DT in terms of the performance indices.

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“…The idea of gradient boosting originated in the observation by Breiman (Breiman, 1997) and can be interpreted as an optimization algorithm based on a suitable cost function. Explicit regression gradient boosting algorithms were subsequently 160 developed (Friedman, 2001;Mason et al, 2000). The boosting algorithm used is described here.…”

Section: The Boosting Algorithmmentioning

confidence: 99%

“…To enhance the accuracy of inflow forecasting and acquiring a longer lead time, increasing amounts of meteorological forecasting data are being used for inflow forecasting (Lima et al, 2017;Fan et al, 2015;Rasouli et al, 2012). However, with extended lead times, the errors of forecast data continuously increase because the variables obtained by numerical weather prediction (NWP) system are also affected by complex factors (Mehr et al, 2019). Moreover, with the continuous improvement of forecasting systems, it is difficult to obtain consistent, long series of forecasting data (Verkade et al, 2013).…”

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confidence: 99%

Multi-step ahead daily inflow forecasting using ERA-Interim reanalysis dataset based on gradient boosting regression trees

Liao¹,

Liu²,

Liu³

et al. 2019

Preprint

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Abstract. Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities make accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework with ERA-Interim reanalysis data as input, adopting gradient boosting regression trees (GBRT) and the maximum information coefficient (MIC) was developed for multi-step ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis dataset provides enough information for the framework to discover inflow for longer lead times. Secondly, MIC can identify effective feature subset from massive features that significantly affects inflow so that the framework can avoid over-fitting, distinguish key attributes with unimportant ones and provide a concise understanding of inflow. Lastly, the GBRT is a prediction model in the form of an ensemble of decision trees and has a strong ability to capture nonlinear relationships between input and output in long lead times more fully. The Xiaowan hydropower station located in Yunnan Province, China is selected as the study area. Four evaluation criteria, the mean absolute error (MAE), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE) and the Pearson correlation coefficient (CORR), were used to evaluate the established models using historical daily inflow data (1/1/2017–31/12/2018). Performance of the presented framework was compared to that of artificial neural networks (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The experimental results indicate that the developed method generally performs better than other models and significantly improves the accuracy of inflow forecasting at lead times of 5–10 days. The reanalysis data also enhances the accuracy of inflow forecasting except for forecasts that are one-day ahead.

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Pareto-optimal MPSA-MGGP: A new gene-annealing model for monthly rainfall forecasting

Cited by 32 publications

References 39 publications

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

Multi-step ahead daily inflow forecasting using ERA-Interim reanalysis dataset based on gradient boosting regression trees

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