Soft computing approaches for forecasting reference evapotranspiration

Gocić, Milan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Ch, Sudheer; Hashim, Roslan; Arif, Muhammad

doi:10.1016/j.compag.2015.02.010

Cited by 160 publications

(67 citation statements)

References 75 publications

(73 reference statements)

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“…Although attempting to model natural phenomena has a long history, the recent application of nature inspired algorithms like firefly algorithm [6], neuro fuzzy technique [7] and genetic programming [8] in the area of soft computing and also recent improvements in forecasting approaches [9,10], has lead to more accurate analysis and predictions, and thereby causing a noticeable growth of interest in this field [11][12][13][14][15][16][17][18]. However, attempts at improving signal extraction and forecasting using bio-inspired algorithms is a relatively new area of research.…”

Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

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Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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show abstract

“…SVM-FFA and SVM-Wavelet (Gocić et al, 2015). Kavousi-Fard and Kavousi-Fard (2013) used a new hybrid method of the cuckoo search algorithm (CSA), autoregressive integrated moving average (ARIMA) and support vector regression (SVR) for short-term load forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Ebtehaj

Bonakdari

Zaji

et al. 2016

Journal of Hydrology and Hydromechanics

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A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

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“…In addition to the empirical equations, soft computing techniques such as artificial neural networks, support vector machines, genetic algorithms and others can be used (GOCIĆ et al, 2015;FENG et al, 2017). These almost always perform better than traditional methods (SHIRI et al, 2014;FENG et al, 2017;MEHDIZADEH et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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ABSTRACT:The reference evapotranspiration (ET o ) is an important component for determining the water requirements of the crops. In order to estimate this variable accurately, the Food and Agriculture Organization (FAO) proposed the Penman-Monteith equation, however, this demands a large number of meteorological data, which restricts its use. In this context, this study compares the performance of the Penman-Monteith equation using only measured air temperature (PMT) and the Hargreaves-Samani (HS) equation with the performance of the multivariate adaptive regression splines (MARS) technique for the daily ET o estimation with only air temperature data. For the study, daily meteorological data from 2002 to 2016 were used. The data were collected from weather stations located in Florianópolis-SC, Manaus-AM and Petrolina-PE, being these selected in order to capture different climatic conditions. MARS models were developed for each weather station and the PMT e HS equations were locally calibrated. The performances of the original and calibrated equations and MARS models were evaluated based on the statistical indices root mean square error, mean absolute error, mean bias error and coefficient of determination. The ET o estimated by the Penman-Monteith method with full data was used as reference for the development of the MARS models, calibration of the equations and for the performance evaluation of the models under study. The calibration of the HS and PMT equations promoted better performances in relation to the original equations, improving the methods accuracy. The MARS technique presented good performance, outperforming the original and calibrated PMT and HS equations, with lower error values and higher coefficient of determination, and can be considered as an alternative to empirical methods.

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Soft computing approaches for forecasting reference evapotranspiration

Cited by 160 publications

References 75 publications

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Reference evapotranspiration estimated from air temperature using the MARS regression technique

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