A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

Abstract

Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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

“…In other words, MLR aims to find the linear function that minimizes the sum of the squares of errors (SSE) between the observed and the predicted data. An advantage of this method is the easy interpretation of the coefficients, which are generated in the model with low computational effort, in comparison to more complex techniques, such as energy balance methods and artificial intelligence algorithms [13][14][15][16][17][18][19][20][21][24][25][26][27][28][29][30][37][38][39][40][41][42][43][67][68][69][70][71][72][73][74][75]. For the MLR model, the response (dependent) variable y is assumed to be a function of k independent variables x i .…”

“…The measurement of the ETo is demanding. Therefore, several methods for estimating ETo have been developed, ranging from simple empirical or physically based models [13,14] to complex algorithms and techniques, such as fuzzy logic and machine learning (ML) [15][16][17][18][19][20][21]. These methods employ data from meteorological stations, or retrieved data via remote sensors [22][23][24][25][26][27][28][29][30][31][32].…”

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

“…The contributions of these variables to ET o prediction will differ depending on the climatic zone. Solar radiation, for example, was found to be the most important contributing variable in the ET o estimation in the majority of climatic zones (Kazemi et al, 2021; Ravindran et al, 2021). Temperature and humidity also contribute considerably to ET o estimation than wind speed in CIMIS datasets (Ravindran et al, 2021).…”

“…Solar radiation, for example, was found to be the most important contributing variable in the ET o estimation in the majority of climatic zones (Kazemi et al, 2021;Ravindran et al, 2021). Temperature and humidity also contribute considerably to ET o estimation than wind speed in CIMIS datasets (Ravindran et al, 2021). However, in ACARR datasets, wind speed plays a significant role in improving the performance of In terms of model structure, the ensemble of model predictions and the weighting method of model ensembles were found to reduce uncertainties and thus produce more robust prediction estimates (Multsch et al, 2015).…”

Trends of CO and NO2 Pollutants Change in Iran during Covid-19 Pandemic using Time-Series Sentinel-5 Images in Google Earth Engine

“…The lockdown was imposed in Iran from March 21 to April 21 in 2020 (Broomandi et al, 2020;Ravindran et al, 2021). During this period, the country witnessed a massive decline in transportation, industrial, and business activities.…”