Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

Abstract

The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

“…The RMSE/MAE and NMRSE are equal to 0.409/0.320 mm d −1 and 12.1%, respectively (Table 2). According to the literature, these values are considered very good to excellent in terms of accuracy [58]. Sunshine hours is a parameter that was computed according to FAO guidelines for missing data, utilizing Julian day and station latitude [33], which might be a limitation in terms of accuracy compared to direct measurements.…”

“…Sharafi and Ghaleni compared the performance of six MLR models to empirical methods. They found that all the MLR models outperformed the latter [58]. The obtained RMSE values, ranging between 0.36 mm d −1 and 0.50 mm d −1 , were considered excellent, so that MLR was recommended for all climate types of Iran, from arid desert to humid [58].…”

“…They found that all the MLR models outperformed the latter [58]. The obtained RMSE values, ranging between 0.36 mm d −1 and 0.50 mm d −1 , were considered excellent, so that MLR was recommended for all climate types of Iran, from arid desert to humid [58]. An MLR model of precipitation, temperature, soil moisture, and NDVI as explanatory variables was applied to four basins in India [59].…”

Hybrid COOT–ANN: a novel optimization algorithm for prediction of daily reference evapotranspiration in Australia

“…A literature review of time series, empirical, and data-driven modeling techniques was conducted to simulate ETo e.g. Haung et al (2019); Fan et al (2018), Nourani et al (2019), Yu et al (2020), Sharafi and Ghaleni, (2021), Douna et al (2021). There have already been several conventional modeling techniques that have proven to be successful in modeling ETo using ANN Algorithm (Achite et al 2022;Gocić and Amiri, 2021;Nawandar et al 2021).…”

Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru

“…Likewise [8] implemented an intelligent temperature and humidity monitoring system for remote environmental monitoring. [9] 1006 (2022) 012009 IOP Publishing doi:10.1088/1755-1315/1006/1/012009 2 evaluated the accuracy of linear regression equations based on meteorological data such as minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation and vapor pressure deficit. In this study we estimate global solar radiation based on environmental variables (temperature, humidity, wind speed) by applying multiple linear regression analysis.…”