Exploring machine learning and multi-task learning to estimate meteorological data and reference evapotranspiration across Brazil

2022 ◽  
Vol 259 ◽  
pp. 107281
Author(s):  
Lucas Borges Ferreira ◽  
Fernando França da Cunha ◽  
Elpídio Inácio Fernandes Filho
Author(s):  
Ali Rashid Niaghi ◽  
Oveis Hassanijalilian ◽  
Jalal Shiri

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.


Author(s):  
Gustavo H. da Silva ◽  
Santos H. B. Dias ◽  
Lucas B. Ferreira ◽  
Jannaylton É. O. Santos ◽  
Fernando F. da Cunha

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.


2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.


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

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.


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

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.


DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 176-183
Author(s):  
Iug Lopes ◽  
Miguel Julio Machado Guimarães ◽  
Juliana Maria Medrado de Melo ◽  
Ceres Duarte Guedes Cabral de Almeida ◽  
Breno Lopes ◽  
...  

The objective was to perform a comparative study of the meteorological elements data that most cause changes in the reference Evapotranspiration (ETo, mm) and its own value, of automatic weather stations AWS and conventional weather stations CWS of the Sertão and Agreste regions of Pernambuco State. The ETo was calculated on a daily scale using the standard method proposed by the Food and Agriculture Organization (FAO), Penman-Monteith (FAO-56). The ETo information obtained from AWS data can be used to update the weather database of stations, since there is a good relationship between the ETo data obtained from CWS and AWS, statistically determined by the Willmott's concordance index (d > 0.7). The observed variations in the weather elements: air temperature, relative humidity, wind speed, and global solar radiation have not caused significant changes in the ETo calculation.


2021 ◽  
Author(s):  
Chaojie Niu ◽  
Xiang Li ◽  
Chengshuai Liu ◽  
Shan-e-hyder Soomro ◽  
Caihong Hu

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.


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