Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Abstract

For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.

Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

“…6 . Taylor diagram shows the standard deviation, RMSE,and Pearson correlation coefficient on a two-dimensional chart, which provides an intuitive way to compare the model performance and reflects the simulation capability of the proposed models 10 , 11 , 18 , 35 . On the whole, Fig.…”

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

“…The success of machine learning models is mainly governed by a good selection of the best predictors, i.e., the best input variables (Malik et al 2019 ; Shukla et al 2021 ; Kushwaha et al 2021 ; Elbeltagi et al 2022b , a; Kumar et al 2022 ). From a general point of view, based on the available input variables, we believe that testing several input combinations is the more suitable procedure for obtaining the best final model; in addition, testing several input combinations can help provide a multitude of alternatives with different structures.…”

“…Over the past two decades, artificial intelligence (AI) and machine learning techniques have been successfully developed and widely used for estimating and predicting (Citakoglu and Coşkun 2022 ), in particular, modeling non-linear hydrologic systems and agriculture field (Shukla et al 2021 ), meteorological droughts and standardized precipitation index (SPI) (Malik et al 2021 ; Xu et al 2022 ), lake water level (Zhu et al 2020 ), rainfall forecasting (Luk et al 2001 ; Olsson et al 2004 ; Abbot and Marohasy 2012 ; Lee et al 2018 ; Mirabbasi et al 2019 ; Adnan et al 2020 ; Armin et al 2021 ; Khosravi et al 2022 ), streamflow forecasting (Yaseen et al 2016 ; Shukla et al 2021 ; Khodakhah et al 2022 ), hydrological drought (Shamshirband et al 2020 ; Aghelpour et al 2021 ; Muhammad et al 2021 ; Almikaeel et al 2022 ), pan evaporation forecasting (Shiri and Özgur 2011 ; Mohammad et al 2019 ; Malik et al 2020 ; Al-Mukhtar 2021 ; Kushwaha et al 2021 ), evapotranspiration (Granata 2019 ; Wu et al 2019 ; Tikhamarine et al 2019 , 2020 ; Chen et al 2020 ; Chia et al 2020 ; Ferreira and da Cunha 2020 ; Elbeltagi et al 2022b ), water level forecasting (Daliakopoulos et al 2005 ; Nayak et al 2006 ; Ali Ghorbani et al 2010 ; Kisi et al 2012 ; Buyukyildiz et al 2014 ; Seo et al 2015 , 2017 ), velocity predictions in compound channels with vegetated floodplains (Harris et al 2003 ), suspended sediment load prediction (Melesse et al 2011 ; Rajaee et al 2011 ; Azamathulla et al 2013 ; Kakaei Lafdani et al 2013 ; Gupta et al 2021 ), soil temperature (Yang and Wang 2008 ; Bilgili 2010 ; Singh et al 2018 ; Penghui et al 2020 ...…”

Deficit irrigation scheduling with mulching and yield prediction of guava (Psidium guajava L.) in a subtropical humid region

“…Among the principal components obtained, those with eigen values greater than 1 were used as independent variables and the yield was used as the dependent variable. All analyses were performed using R software, version 4.0.5 (Shukla et al, 2021;Elbeltagi et al, 2022).…”