Reference Evapotranspiration Modeling Using New Heuristic Methods

Chen, Zhihuan; Yuan, Xiao; Kisi, Özgür; El-Shafie, Ahmed; Kuriqi, Alban; Adnan, Rana Muhammad

doi:10.3390/e22050547

Cited by 34 publications

(15 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, depending on the station and test year, this similarity becomes even closer. All the test years and stations showed an improvement from temperature to radiation or mass-transfer-based combination except for the Prosper station, which is in agreement with the findings of Adnan et al [15]. On the other hand, the Prosper station showed the best improvement for the SVM model for radiation-based approaches.…”

Section: Discussionsupporting

confidence: 89%

“…The ET o calculation is a complex process due to a large number of associated meteorological variables, and it is hard to develop an accurate empirical model to overcome all the complexities of the process [15]. Over the last few decades, machine learning (ML) techniques have attracted the interest of streams of researchers around the globe to overcome the ET a estimation complexity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

2021

View full text Add to dashboard Cite

Evapotranspiration (ET) is widely employed to measure amounts of total water loss between land and atmosphere due to its major contribution to water balance on both regional and global scales. Considering challenges to quantifying nonlinear ET processes, machine learning (ML) techniques have been increasingly utilized to estimate ET due to their powerful advantage of capturing complex nonlinear structures and characteristics. However, limited studies have been conducted in subhumid climates to simulate local and spatial ETo using common ML methods. The current study aims to present a methodology that exempts local data in ETo simulation. The present study, therefore, seeks to estimate and compare reference ET (ETo) using four common ML methods with local and spatial approaches based on continuous 17-year daily climate data from six weather stations across the Red River Valley with subhumid climate. The four ML models have included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF) with three input combinations of maximum and minimum air temperature-based (Tmax, Tmin), mass transfer-based (Tmax, Tmin, U: wind speed), and radiation-based (Rs: solar radiation, Tmax, Tmin) measurements. The estimates yielded by the four ML models were compared against each other by considering spatial and local approaches and four statistical indicators; namely, the root means square error (RMSE), the mean absolute error (MAE), correlation coefficient (r2), and scatter index (SI), which were used to assess the ML model’s performance. The comparison between combinations showed the lowest SI and RMSE values for the RF model with the radiation-based combination. Furthermore, the RF model showed the best performance for all combinations among the four defined models either spatially or locally. In general, the LR, GEP, and SVM models were improved when a local approach was used. The results showed the best performance for the radiation-based combination and the RF model with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among the models and approaches.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

2021

View full text Add to dashboard Cite

show abstract

“…A review of the literature indicates that numerous studies have examined the application of several AI models for estimating ET 0 [17][18][19][20][21][22][23][24][25][26][27][28]. In more detailed state-of-the-art, Yin et al [26] introduced a new hybrid AI model dependent on the hybridization of a genetic algorithm with a kernel model i.e., a support vector machine (GA-SVM) for modeling daily ET 0 in China using several daily climatic variables including T max , T min , wind speed (U 2 ), relative humidity (RH), and solar radiation (SR).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

Self Cite

View full text Add to dashboard Cite

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

show abstract

“…The finding of that study showed that all models provide satisfactory results in terms of several performance criteria. Adnan, et al [6] predicted reference evapotranspiration (ET0) in the China by evaluating different data driven models based solely on-air temperature data. Although there is plethora of studies that have already explored the potential use of several intelligent models at different sub-filed of water resources management, the present study aims to bring into light the potential of some other novel intelligent models which to the best of our knowledge are scarcely applied in the field of climatology, particularly in the air temperature prediction.…”

Section: Introductionmentioning

confidence: 99%

Air temperature prediction using different machine learning models

Adnan

Liang

Kuriqi

et al. 2021

IJEECS

Self Cite

View full text Add to dashboard Cite

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

show abstract

Reference Evapotranspiration Modeling Using New Heuristic Methods

Cited by 34 publications

References 51 publications

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Air temperature prediction using different machine learning models

Contact Info

Product

Resources

About