Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

Mehdizadeh, Saeid

doi:10.1016/j.jhydrol.2018.02.060

Cited by 82 publications

(26 citation statements)

References 51 publications

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“…Likewise, Tabari et al (2015) reported better performance of an AI-based model including the MLP using the antecedent ST data for modelling the ST at deeper layers than the surface layers. Furthermore, the results of the current study verify the outcomes of previous studies such as Mehdizadeh (2018b), Mehdizadeh and Kozekalani Sales (2018), Mehdizadeh et al (2017cMehdizadeh et al ( , 2018bMehdizadeh et al ( , 2019, and Fathian et al (2019). The authors developed different types of the hybrid models through hybridization of the different time-series-and AI-based models for improving the modelling efficiency of classical models in modelling hydrological variables.…”

Section: Performance Evaluation Of the Classical And Hybrid Modelssupporting

confidence: 91%

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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Section: Performance Evaluation Of the Classical And Hybrid Modelssupporting

confidence: 91%

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

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

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“…In order to integrate flux observations to the longer time intervals needed for evaluating water and carbon budgets, gaps introduced by instrument downtime and invalid measurements must be filled in. Machine learning has been well developed for predicting ET since the early-2000s (Whitley et al, 2009;Mehdizadeh, 2018). In this study, we adopted RandomForest for the gap-filling (Breiman, 2001).…”

Section: Machine Learning Prediction Of Water Fluxesmentioning

confidence: 99%

“…Trained by a set of fundamental micrometeorological variables, the RandomForest algorithm yields overall accurate predictions of ET in boreal larch forest. However, we emphasize that its application to other ecosystems still needs to be further verified and spatial and temporal scales of input data are an important factor limiting the performance of machine learning algorithms (Whitley et al, 2009;Mehdizadeh, 2018).…”

Section: Flux Gap Filling and Nighttime Transpiration In Boreal Larchmentioning

confidence: 99%

Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest

Liu

Cheng

Munger

et al. 2020

Agricultural and Forest Meteorology

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“…They resulted RMSE between 0.222 to 0.555 mm day −1 . In addition, Mehdizadeh [55] reported RMSE for GEP models from 0.46 to 2.08 mm day −1 .…”

Section: Performance Of Gene Expression Programming (Gep) For Mean Tementioning

confidence: 99%

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

et al. 2019

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In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day −1 ) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day −1 , and Kerman (KE), RMSE = 0.4177 mm day −1 , respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R 2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R 2 < 0.50) due to its poor performance under such extreme conditions. Atmosphere 2019, 10, 311 2 of 15 sediment [9], predicting velocity in compound channels [10], characterizing risks in water supply systems [11], and modeling evaporation [12].Although the GEP has been developed in water resources studies, the application of this technique for ETo modeling is limited. Some of the successful applications of the GEP to estimate ETo can be listed as follows.Parasuraman et al. [3] modeled ETo using only ground temperature and net radiation. Irmak and Kamble [13] investigated evapotranspiration data assimilation with the GA and soil, water, atmosphere, and plant (SWAP) model for on-demand irrigation. The data assimilation methodology obtained from the present research can be considered a practical tool at the field scale for scheduling the irrigation estimated by remote sensing-based evapotranspiration. The results showed that the GA was effectively able to determine the terms included in the fitness function, and parameters were predicted reasonably, especially if only four variables were included. Traore and Guven [14] developed regional-specific numerical models of ETo using the GEP in Sahel. Statistically, the GEP was an effectual modeling tool for the successful computation of ETo under the study area. The results indicated that, using the GEP model, it would be possible to formulate an accurate and applicable numerical equation for each region by irrigation systems for the manual computation of ETo under the study area, where sufficient meteorological variable is often missing. Shiri [15] claimed that GEP outperforms empirical model to calculate ETo in hyper arid regions over Iran. Traore et al. [16] found that GEP is a robust tool to model ETo in in Jiangsu province, China. Mattar [17] demonstrated that GEP is a more accurate method than empirical equations to ...

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Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

Cited by 82 publications

References 51 publications

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

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

Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

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