Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths

Samadianfard, Saeed; Asadi, Ebrahim; Jarhan, Salar; Kazemi, Honeyeh; Kheshtgar, Salar; Kişi, Özgür; Sajjadi, Shahin; Manaf, Azizah Abdul

doi:10.1016/j.still.2017.08.012

Cited by 84 publications

(37 citation statements)

References 23 publications

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“…There is no basic and technical way of separating training and testing data. For example, the study by Kurup and Dudani [32] used 63% of total data for model development, whereas Pal [33] used 69%, Samadianfard et al [20,21,34] used 67% of total data, and Deo et al [35] and Samadianfard et al [36] used 70% of the total data to develop their models. Thus, to develop the studied models, the data are divided into training (70%) and testing (30%).…”

Section: Study Areamentioning

confidence: 99%

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Samadianfard

Jarhan

Salwana

et al. 2019

Water

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Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m 3 /s), MAE (2.40 and 3.71 m 3 /s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q t−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

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Section: Study Areamentioning

confidence: 99%

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Samadianfard

Jarhan

Salwana

et al. 2019

Water

Self Cite

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“…GEP evolves computer programing in different forms consisting decision trees and logical and mathematical expressions [45][46][47]. Moreover, the GEP approach has high performance in solving a large number of problems in different fields especially water engineering [48][49][50][51]. Here, mathematical equations based on the GEP model have been applied to predict daily streamflow records in Karaj basin, Iran.…”

Section: Gene Expression Programmingmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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“…The results illustrated that all three methods were capable of estimating the ST well, and among these methods, the ANN had superior results. Samadianfard et al (2018) used wavelet neural networks (WNN), ANN and GEP models for estimating the daily ST at Tabriz station, Iran. Their results indicated that the WNN had superior outcomes compared with the ANN and GEP.…”

Section: Introductionmentioning

confidence: 99%

Modelling daily soil temperature at different depths via the classical and hybrid models

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

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Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.

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Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths

Cited by 84 publications

References 23 publications

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Modelling daily soil temperature at different depths via the classical and hybrid models

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