Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

Ahmadi, Farshad; Mehdizadeh, Saeid; Mohammadi, Babak; Pham, Quoc Bao; Doan, Thi Ngoc Canh; Vo, Ngoc Duong

doi:10.1016/j.agwat.2020.106622

Cited by 77 publications

(33 citation statements)

References 55 publications

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“…In addition to the empirical models, machine learning (ML) approaches have recently received remarkable attention in modeling the ETo, and have shown reasonable performances. The ML techniques are capable of capturing hydrological time series such as ETo by utilizing solely a series of predictors without any knowledge of their physical processes [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Mehdizadeh

Mohammadi

Pham

et al. 2021

Water

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Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

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Section: Introductionmentioning

confidence: 99%

“…Other types of coupled models developed via hybridizing the ML and optimization algorithms have been recently proposed to improve ETo modeling. For example, interested readers can refer to Ahmadi et al [13], Roy et al [32], Chia et al [33], Yan et al [34], Gong et al [35], Gao et al [36], and Dong et al [37].…”

Section: Introductionmentioning

confidence: 99%

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Mehdizadeh

Mohammadi

Pham

et al. 2021

Water

Self Cite

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“…In recent years, despite the presence of some well-known mathematical models such as Penman-Monteith, Thornthwaite, Hargreaves-Samani, Blaney-Criddle, etc., the black-box artificial intelligence (AI) models have been able to show acceptable accuracy in estimating evapotranspiration. For example, Mohammadi & Mehdizadeh (2020) and Ahmadi et al (2021) by carrying out a survey on the arid and semi-arid regions of Iran found that in the complete absence of meteorological variables (which are required to use the Penman method), the AI models are able to estimate evapotranspiration with reasonable accuracy, by the least available meteorological variables. They also contended that integrating AI models with bio-inspired optimization algorithms can significantly increase the accuracy of evapotranspiration estimation.…”

Section: Introductionmentioning

confidence: 99%

Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

Aghelpour

Varshavian

Hamedi

2021

Preprint

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Reference crop evapotranspiration (ET0) is one of the most important hydro-climatological components which directly affects agricultural productions, and its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly ET0. Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on monthly scale during 1995-2018, based on FAO-56 Penman-Monteith equation and meteorological data including: minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including: Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid) and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE) and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models, and superior compared to its other competitors. Consequently, due to the simplicity and parsimony, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

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“…RMSE of SVR, GEP, and GMDH models forecasts was 12, 18 and 10 mm, respectively in configuration two for Tabriz. For estimation of monthly reference evapotranspiration in Iran, SVR had good performance than GEP (Ahmadi et al, 2021). A study investigates the performance of the multivariate regression spline, least-square support vector regression, GEP and ANN for estimation of monthly long-term rainfall and the best model was least square support vector regression (Mirabbasi et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

Parviz¹,

Rasouli²,

Torabi³

2021

Preprint

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Precipitation forecast, especially on monthly and annual scales, is a key for optimal water resources management and planning, especially in semiarid climates with scarce water. The traditional hybrid models, in which two statistical models are used to separate and simulate linear and nonlinear components of precipitation time series, are still unable to provide accurate precipitation forecasts. This research aims to improve hybrid forecast models by combining one linear model and three nonlinear models with two preprocessing configurations: 1) using residuals of a linear model, representing the nonlinear component with different time steps and 2) using original time series of observations with different time steps, linear model simulations and residuals. Gene Expression Programming (GEP), Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) models were used individually as in the traditional hybrid models and combinedly as in the proposed hybrid models in this study. The performance of the hybrid models was improved by different methods such as inverse variance (Iv) as an error-based method, least square regression, genetic algorithm and SVR. Two weather stations of Tabriz (annual) and Rasht (monthly) in Iran were selected to test the developed models. The results showed that Theil’s coefficient, UII, decreased in configuration one for the Tabriz station by 9% and 15% for SVR and GMDH relative to GEP, suggesting that these two models performed better than GEP in the precipitation forecast. The error criteria used in developing the proposed hybrid models with all forecast combination methods better represent observations than the hybrid model. MSE decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station in configuration two when we combined the three models using GA to obtain the improved hybrid model relative to the hybrid model combined with SVR. Generally, the hybrid models when SVR, the error based methods and GA were incorporated showed better performance than traditional hybrid models. The developed models have implications for modeling highly nonlinear systems using full advantages of machine learning methods.

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Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

Cited by 77 publications

References 55 publications

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

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