A deep learning based framework for enhanced reference evapotranspiration estimation: evaluating accuracy and forecasting strategies

Abstract

Affordable and efficient agricultural methods enhance crop yield and water management by optimizing resources. Precise irrigation relies on accurate estimation of reference evapotranspiration (ETo). Numerous analytical and empirical methods exist to compute ETo but these methods are costlier, requires time and perform poorly under limited availability of meteorological data. This study first evaluated the performances of three deep learning sequential models—Long short-term memory (LSTM), Neural Basis Expansion Analysis for Time Series (N-BEATS) and, Temporal Convolutional Network model (TCN), for predicting daily ETo possessing temporal characteristics. In this TCN is considered as baseline model to be compared with other models. In the results, TCN performed better, so it is further utilized to evaluate two strategies of ETo prediction that makes the second objective of the paper. In the first approach, historic data is used to predict future ETo using TCN which is standard method. And, in recursive approach, TCN predicted climatological data and, ETo is computed. This is required for better irrigation planning in data-scarce situations. The results demonstrate that the TCN model provided satisfactory performance with the Nash–Sutcliffe Efficiency (NSE) = 0.99, Theil U2 = 0.005, RMSE = 0.092 and, MAE = 0.048. Also, with the recursive strategy, ETo values computed found more accurate than using standard approach. Thus, comparative study among sequential architecture revealed TCN outperformed LSTM and N-BEATS models and, is an efficient method for predicting ETo time-series and, could also assist in the precise management of water resources in data scarcity.