Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment

Elbeltagi, Ahmed; Deng, Jinsong; Wang, Ke; Malik, Anurag; Maroufpoor, Saman

doi:10.1016/j.agwat.2020.106334

Cited by 76 publications

(22 citation statements)

References 63 publications

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“…These results of the present study confirmed the validity and usefulness of the use of the R-SPAC model for estimating ET 0 in a reference agricultural catchment. Although previous studies have shown similar performances to that of the current study through the use of other approaches [8,9,19,36,37], the use of the R-SPAC model offers various advantages. Firstly, the approach proposed in the present study allows the consideration and parameterization of plant factors that affect ET, including crop type, continuous growth processes characterized by leaf area index and canopy height development, and crop canopy structure that affects the plant canopy and geometry.…”

Section: Modeling Advantages and Limitationssupporting

confidence: 63%

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Wang

et al. 2021

Agriculture

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To estimate the irrigation volume required for agriculture and improve water resources utilization efficiency, it is essential to obtain an estimate of reference evapotranspiration (ET0) and its components (e.g., reference transpiration, T0 and reference soil evaporation, E0). This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and its associated components to obtain a reference-based SPAC model of reference evapotranspiration (R-SPAC), and it applied the model to an agricultural ecosystem. Model simulations of mean hourly ET0 were benchmarked against those of the Penman-Monteith method by the Food and Agriculture Organization (FAO-PM) throughout the growing season. The resulting good correlation obtained (R2 = 0.96, agreement index, I = 0.98, root-mean-square deviation (RMSD) = 0.05 mm h−1) validated the accuracy of the R-SPAC model. Sensitivity analysis was used to explore uncertainties and errors for ET0, T0, and E0 caused by input variables. The results showed that net radiation and shortwave radiation at the study site were the main drivers of ET0 for both the FAO-PM and R-SPAC models. The study showed that the proposed R-SPAC model can be used for predicting ET0 and for exploring interactions between climate, crop type, and soil in determining evapotranspiration under various future environment conditions.

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Section: Modeling Advantages and Limitationssupporting

confidence: 63%

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Wang

et al. 2021

Agriculture

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“…As the name indicates, it is the square root of the mean square error. A zero value indicates the best prediction and can be expressed as (Elbeltagi et al 2020;Malik et al 2021c…”

Section: Performance Evaluation Indicatorsmentioning

confidence: 99%

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

et al. 2021

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In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

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“…Taylor diagram, radar-chart, temporal plots). The MAE (Elbeltagi et al, 2020;Rehamnia et al, 2021), RMSE (Abba et al, 2021;Malik et al, 2021a;Pandey et al, 2020), IOS (Malik et al, 2019;Tao et al, 2018), NSE (Nash & Sutcliffe, 1970), PCC (Malik et al, 2020b(Malik et al, , 2021b, and IOA (Tikhamarine et al, 2020;Willmott, 1981) are stated as:…”

Section: Integrated Hybrid Svr Models and Goodness-of-fit Measuresmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment

Cited by 76 publications

References 63 publications

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

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