Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration

Tikhamarine, Yazid; Malik, Anurag; Souag-Gamane, Doudja; Kişi, Özgür

doi:10.1007/s11356-020-08792-3

Cited by 89 publications

(41 citation statements)

References 63 publications

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“…at totally depends on the historical data memory provided by the lead time "antecedent river flow values." A notable enhancement is achieved using the hybridized SVR-GA which collaborates with the findings of several other studies established over the literature within hydrological engineering [69][70][71][72]. It is observed that the data division plays an essential role in the learning process of the developed ML models.…”

Section: Model Performance Evaluation Using Statisticalsupporting

confidence: 75%

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

et al. 2020

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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.

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Section: Model Performance Evaluation Using Statisticalsupporting

confidence: 75%

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

et al. 2020

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“…Since its ability to seek out the complicated nonlinear relationships between the given datasets, the ANNs can be applied to complex systems' modeling tasks. In the hydrological field, the ANNs have been used for different aims, for instance, flood or runoff forecasting [17,[22][23][24], rainfall forecasting [25][26][27], and evapotranspiration prediction [28,29].…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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“…It enthused their basic concept from the behaviors of bird flocks. The PSO could be used in different fields of optimization, such as multiple-objective optimization, nonlinear and stochastic problems (Malik et al, 2020d;Tikhamarine et al, 2019Tikhamarine et al, , 2020. The working assembly of PSO could be summarized in the following steps:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…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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Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration

Cited by 89 publications

References 63 publications

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

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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