scholarly journals Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Ping Jiao ◽  
Shun-Jun Hu
Keyword(s):  
Empirical Model ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Input Parameter ◽  
Accurate Estimation ◽  
Climate Data ◽  
Northern Xinjiang ◽  
Model Based ◽  
Bp Model ◽  
Resources Planning

Accurate estimation of reference evapotranspiration is a key step in irrigation and water resources planning. The Penman Monteith (FAO56-PM) formula recommended by FAO56-PM is the standard for calculating the reference evapotranspiration. However, the FAO56-PM model is limited in the observation of meteorological variables, so it is necessary to choose an alternative ET0 model which requires less meteorological data. Based on the daily climate data of eight meteorological stations in northern Xinjiang from 2000 to 2020, seven empirical models (Hargreaves, Berti, Dorji, Dalton, Meyer, WMO, Albrecht) and four optimization algorithms (RF model, LS-SVR model, Bi-LSTM model and GA-BP model) combined with seven different parameters were evaluated comprehensively. The results show that the accurate of the empirical model based on temperature is obviously better than the empirical model based on air mass transport. The annual and multi-year alternative ET0 models of different input parameter combinations are: LS-SVR1, RF2, LS-SVR3, LS-SVR4, GA-BP5, LS-SVR6, GA-BP7. It can be used as a substitute for the reference evapotranspiration model without relevant meteorological data. Only the LS-SVR6 model and GA-BP7 model are recommended as the best alternative models for northern Xinjiang reference evapotranspiration at daily, monthly and seasonal scales.

Estimation and Comparative Analysis of Daily Reference Evapotranspiration Based on Different Input Combinations and Different Models

2021 ◽  
Author(s):  
Chaojie Niu ◽  
Xiang Li ◽  
Chengshuai Liu ◽  
Shan-e-hyder Soomro ◽  
Caihong Hu
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Maximum Temperature ◽  
Least Squares Regression ◽  
Simulation Performance ◽  
Bp Model ◽  
Plsr Model

Abstract Daily reference evapotranspiration (ET0) is the most crucial link in estimating crop water demand. In this study, Levenberg-Marquardt (L-M), Genetic Algorithm-Back Propagation (GA-BP) and Partial Least Squares Regression (PLSR) models were introduced to calculate the ET0 values, Based on the Pearson Correlation analysis method, five meteorological factors were obtained, which were combined into six different input scenarios. Compared with the values that calculated by the the Penman Monteith (PM) formula. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) were used to evaluate the simulation performance of the models. The results showed that the simulation effect of the L-M model is better than that of the GA-BP model and PLSR model in all scenarios. PLSR model has the worst performance. The SI index of L-M6 was 46.69% lower than that of GA-BP6 and 65.78% lower than that of PLSR6. When the input factors are 3, the simulation effect of the input wind speed, the maximum temperature and the minimum temperature is the best. L-M model and GA-BP model can predict the ET0 in the region with a lack of meteorological data. This study provides an important reference for high-precision prediction of ET0 under different input combinations of meteorological factors.

scholarly journals Coupling a Bat Algorithm with XGBoost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yixiu Han ◽  
Jianping Wu ◽  
Bingnian Zhai ◽  
Yanxin Pan ◽  
Guomin Huang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Bat Algorithm ◽  
Irrigation Scheduling ◽  
Multivariate Adaptive Regression Splines ◽  
Accurate Estimation ◽  
Agricultural Irrigation ◽  
Semiarid Regions ◽  
Arid And Semiarid

Accurate estimation of reference evapotranspiration (ETo) is key to agricultural irrigation scheduling and water resources management in arid and semiarid areas. This study evaluates the capability of coupling a Bat algorithm with the XGBoost method (i.e., the BAXGB model) for estimating monthly ETo in the arid and semiarid regions of China. Meteorological data from three stations (Datong, Yinchuan, and Taiyuan) during 1991–2015 were used to build the BAXGB model, the multivariate adaptive regression splines (MARS), and the gaussian process regression (GPR) model. Six input combinations with different sets of meteorological parameters were applied for model training and testing, which included mean air temperature (Tmean), maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U), relative humidity (RH), and solar radiation (Rs) or extraterrestrial radiation (Ra, MJ m−2·d−1). The results indicated that BAXGB models (RMSE = 0.114–0.412 mm·d−1, MAE = 0.087–0.302 mm·d−1, and R2 = 0.937–0.996) were more accurate than either MARS (RMSE = 0.146–0.512 mm·d−1, MAE = 0.112–0.37 mm·d−1, and R2 = 0.935–0.994) or GPR (RMSE = 0.289–0.714 mm·d−1, MAE = 0.197–0.564 mm·d−1, and R2 = 0.817–0.980) model for estimating ETo. Findings of this study would be helpful for agricultural irrigation scheduling in the arid and semiarid regions and may be used as reference in other regions where accurate models for improving local water management are needed.

scholarly journals Global comparison of 20 reference evapotranspiration equations in a semi-arid region of Iran

2018 ◽  
Vol 50 (1) ◽  
pp. 282-300 ◽  
Author(s):  
Hadi Farzanpour ◽  
Jalal Shiri ◽  
Ali Ashraf Sadraddini ◽  
Slavisa Trajkovic
Keyword(s):  
Arid Region ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Scheduling ◽  
Climatic Conditions ◽  
Accurate Estimation ◽  
Management Scenarios ◽  
Global Comparison ◽  
Semi Arid Region ◽  
Semi Arid

Abstract Accurate estimation of reference evapotranspiration (ETo) is a major task in hydrology, water resources management, irrigation scheduling and determining crop water requirement. There are many empirical equations suggested by numerous references in literature for calculating ETo using meteorological data. Some such equations have been developed for specific climatic conditions while some have been applied universally. The potential for usage of these equations depends on the availability of necessary meteorological parameters for calculating ETo in different climate conditions. The focus of the present study was a global cross-comparison of 20 ETo estimation equations using daily meteorological records of 10 weather stations (covering a period of 12 years) in a semi-arid region of Iran. Two data management scenarios, namely local and cross-station scenarios, were adopted for calibrating the applied equations against the standard FAO56-PM model. The obtained results revealed that the cross-station calibration might be a good alternative for local calibration of the ETo models when proper similar stations are used for feeding the calibration matrix.

scholarly journals Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

2019 ◽  
Author(s):  
Ali Rashid Niaghi ◽  
Oveis Hassanijalilian ◽  
Jalal Shiri
Keyword(s):  
Machine Learning ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Machine Learning Techniques ◽  
Limiting Factors ◽  
Support Vector ◽  
Daily Maximum ◽  
Climate Data ◽  
Input Variables

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

scholarly journals Estimating models for reference evapotranspiration with core meteorological parameters via path analysis

2016 ◽  
Vol 48 (2) ◽  
pp. 340-354 ◽  
Author(s):  
Ye Liu ◽  
Miao Yu ◽  
Xiaoyi Ma ◽  
Xuguang Xing
Keyword(s):  
Neural Network ◽  
Path Analysis ◽  
Meteorological Parameters ◽  
Reference Evapotranspiration ◽  
Southern China ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Decision Variable ◽  
Cent Error

Accurate estimation and reliable universal performance of reference evapotranspiration (ET0) obtained from a few meteorological parameters are important for the rational planning of agricultural water resources and the effective management of water in irrigated regions. Meteorological data in southern China were used to calculate ET0 using the standard Penman–Monteith formula and determined the core decision variable (hours of sunshine, N) and the limited decision variable (relative humidity, RH) using path analysis. Estimation models using an artificial neural network and wavelet neural network were established for the Wuhan and Guangzhou meteorological stations. The statistical indices were positively correlated with the decision contribution rates to ET0. The ET0 values for other stations in southern China were all estimated by these models, which were trained for the Guangzhou station, and then made a total comparison with Hargreaves–Samani (HS) and Priestley–Taylor (PT) empirical ET0 models. Error analysis indicated that the root mean square error and the mean absolute per cent error were around 0.32 mm and 5.5%, respectively, with a high coefficient of determination and Nash–Sutcliffe efficiency over 0.9, indicating that these estimating models could be applied in more regions for universal analysis with high accuracy.

Empirical model based on the measurements of the Japanese spacecrafts

1999 ◽  
Vol 30 (5) ◽  
pp. 617-624 ◽  
Author(s):  
Tateo Goka ◽  
Haruhisa Matsumoto ◽  
Shunji Takagi
Keyword(s):  
Empirical Model ◽  
Model Based

scholarly journals Theoretical models of inhibitory activity for inhibitors of protein–protein interactions: targeting menin–mixed lineage leukemia with small molecules

MedChemComm ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 2216-2227 ◽  
Author(s):  
Wiktoria Jedwabny ◽  
Szymon Kłossowski ◽  
Trupta Purohit ◽  
Tomasz Cierpicki ◽  
Jolanta Grembecka ◽  
...  
Keyword(s):  
Small Molecules ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Empirical Model ◽  
Inhibitory Activity ◽  
Theoretical Models ◽  
Mixed Lineage Leukemia ◽  
Protein Protein Interactions ◽  
Model Based ◽  
Dispersion Terms

A computationally affordable, non-empirical model based on electrostatic multipole and dispersion terms successfully predicts the binding affinity of inhibitors of menin–MLL protein–protein interactions.

scholarly journals Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil

2018 ◽  
Vol 22 (2) ◽  
pp. 83-89 ◽  
Author(s):  
Gustavo H. da Silva ◽  
Santos H. B. Dias ◽  
Lucas B. Ferreira ◽  
Jannaylton É. O. Santos ◽  
Fernando F. da Cunha
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Standard Method ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Air Humidity ◽  
Method Performance ◽  
Relative Air Humidity ◽  
Evapotranspiration Estimation ◽  
Missing Variables

ABSTRACT FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET0) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott’s index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance. Methods that used only air temperature data are not recommended for the region.

scholarly journals Reference evapotranspiration for Londrina, Paraná, Brazil: performance of different estimation methods

2017 ◽  
Vol 38 (4Supl1) ◽  
pp. 2363
Author(s):  
Rodrigo Dlugosz da Silva ◽  
Marcelo Augusto de Aguiar e Silva ◽  
Marcelo Giovanetti Canteri ◽  
Juliandra Rodrigues Rosisca ◽  
Nilson Aparecido Vieira Junior
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Global Radiation ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Performance Indices ◽  
Excellent Performance ◽  
Study Region ◽  
Temperature Radiation ◽  
And Performance

Aiming at assessing the performance of alternative methods to Penman-Monteith FAO56 for estimating the reference evapotranspiration (ETo) for Londrina, Paraná, Brazil, the methods temperature radiation, Hicks-Hess, Hargreaves-Samani (1982), Turc, Priestley-Taylor, Tanner-Pelton, Jensen-Haise, Makkink, modified Hargreaves, Stephens-Stewart, Abtew, global radiation, Ivanov, Lungeon, Hargreaves-Samani (1985), Benavides-Lopez, original Penman, Linacre, Blaney-Morin, Romanenko, Hargreaves (1974), McCloud, Camargo, Hamon, Kharrufa, McGuiness-Bordne, and Blaney-Criddle were compared to that standard method recommended by FAO. The estimations were correlated by linear regression and assessed by using the Person’s correlation coefficient (r), concordance index (d), and performance index (c) using a set of meteorological data of approximately 40 years. The methods modified Hargreaves, Stephens-Stewart, Abtew, global radiation, Ivanov, Lungeon, Hargreaves-Samani (1985), Benavides-Lopez, original Penman, and Linacre should be avoided, as they did not present excellent results. The methods McCloud, Camargo, Hamon, Kharrufa, McGuinness-Bordne, Blaney-Criddle, Hargreaves (1974), Romanenko, and Blaney-Morin were classified as very bad, not being recommended. In contrast, the methods temperature radiation, Hicks-Hess, Hargreaves-Samani (1982), Turc, Priestley-Taylor, Tenner-Pelton, Jensen-Haise, and Makkink presented excellent performance indices and can be applied in the study region.

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