An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

• Published in: Hydrological processes 2022-05, Vol.36 (5), p.n/a
• Main Authors: Mangalath Ravindran, Sowmya, Moorakkal Bhaskaran, Santosh Kumar, K. Ambat, Sooraj, Balakrishnan, Kannan, Manguttathil Gopalakrishnan, Manoj
• Format: Article
• Language: English
• Subjects: AutoGluon‐tabular; Automation; AutoML; Computer applications; Data science; Datasets; deep learning; Empirical analysis; Empirical models; Errors; Evapotranspiration; Evapotranspiration models; H2O; Hydrologic models; Hydrologic processes; Hydrology; Information management; Information systems; Irrigation systems; Learning algorithms; Machine learning; Management information systems; Mathematical models; Meteorological data; Modelling; Parameters; Performance enhancement; Performance evaluation; Pipelines; Radiation; reference evapotranspiration; Submarine pipelines; Tropical climate; Water allocation; Water management; Weather stations
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.14571

An accurate and minimal meteorological parameter‐based reference evapotranspiration (ETo) computational model is highly essential to design a cost‐effective and automated solution for precise water allocation in various agricultural and hydrological applications. Automated machine learning (AutoML), a substantially progressive machine learning (ML) paradigm, expedites the data science applications in these domains by automating and alleviating the tedious tasks of developing ML pipelines, particularly in non‐linear hydrological process modelling. This paper proposes an AutoML solution for daily EToprediction in a limited input parameter scenario, for the first time in EToestimation research. Two AutoML frameworks, AutoGluon‐Tabular (AGT) and H2O AutoML were implemented using daily meteorological data of a humid tropical climatic region of Kerala, India and evaluated its performance with the radiation‐based empirical methods and traditional ML methods. The developed AutoML models imitated the input parameter combinations of radiation‐based empirical models such as Priestley‐Taylor, Makkink, Turc and Ritchie, with the Penman‐Monteith EToserving as the target for modelling process. An improvement of mean absolute error from 0.119 to 0.078 mm/day, mean squared error from 0.035 to 0.017 mm/day, root mean squared error from 0.186 to 0.129 mm/day, root mean squared logarithmic error from 0.078 to 0.026 mm/day, and coefficient of determination (R2) from 0.971 to 0.985 observed in the AutoML model, AGT relative to the empirical model, Turc describes its enhanced performance capability. For demonstrating the superiority of AutoML to traditional ML techniques, another set of AutoML models were also developed based on California Irrigation Management Information System datasets and evaluated them with existing study results in literature. The overall result analysis demonstrated the superiority of AGT model in EToprediction in all the weather stations considered in this study. The results of the study confirm that AutoML techniques can be applied to any hydrological dataset and provide an effective automated solution for hydrological process modelling. AutoML techniques such as AutoGluon‐Tabular and H2O AutoML were implemented to estimate daily ETo in the limited input scenario using ACARR and CIMIS datasets. The evaluation process with radiation‐based empirical methods and state‐of‐the‐art study results highlighted the potential of AutoML models in ETo modelling. Aut