Estimation of Reference Crop Evapotranspiration with Three Different Machine Learning Models and Limited Meteorological Variables

Abstract

Precise reference crop evapotranspiration (ET0) estimation plays a key role in agricultural fields as it aids in the proper operation and management of irrigation scheduling. However, reliable ET0 estimation poses a challenge when there is insufficient or incomplete long-term meteorological data at the East Coast Economic Region (ECER), Malaysia, where the economy is highly dependent on agricultural crop production. This study evaluated the performances of different standalone machine learning (ML) models, namely, the light gradient boosting machine (LGBM), decision forest regression (DFR), and artificial neural network (ANN) models using four different combinations of meteorological variables. The incorporation of solar radiation enhanced the accuracy of the standalone ML models, demonstrating the role of energetic factors in the evapotranspiration mechanism. Additionally, both the ANN and LGBM models showed overall satisfactory performances, and were thus recommended them as alternate models for ET0 estimation. This was owing to their good capability in capturing the non-linearity and interaction process among the meteorological variables. The outcomes of this study will be advantageous to farmers and policymakers in determining the actual crop water demands to maximize crop productivity in data-scarce tropical regions.

Comparative Assessment of Eight Empirical and Four Hybrid Machine Learning Models for Estimating Daily Reference Evapotranspiration in Sub-Humid and Semi-Arid Climates

“…For Loukkos, the ranking was: LightGBM6>XGBoost6>LightGBM3>XGBoost3 with average RMSE of 0.025 -0.027 mm.day −1 . These findings support previous studies by Fan et al [32] and Yong et al [28], where LightGBM consistently outperformed other standalone ML models with an RMSE of 0.08-0.58 mm.day −1 and 0.041-0.315 mm.day −1 , respectively. Further, there was a minor difference between…”

“…Goyal et al [27] highlighted the promising findings of ML models in various climates and environments, emphasizing their ability to improve accuracy above standard empirical models. Besides, researchers have applied various ML models, such as artificial neural networks (ANN) [28,29], support vector regression (SVR) [30], M5 model tree [31], random forests (RF) [32], reduced error pruning tree (REPTree) [33], extreme gradient boosting (XGBoost) [34], light gradient boosting machine (LightGBM) [28] and decision trees (DT) [35] to estimate daily RET uing restricted meteorological data.…”

“…Their findings revealed that the GBDT and XGBoost models exhibited performance on par with the SVM and ELM models, while offering advantages in terms of simplicity, accuracy, stability, and reduced computational costs, making them recommended options for daily RET estimation. Additionally, Yong et al [28] evaluated performance of LightGBM, ANN and decision forest regression (DFR) in five Malaysian meteorological stations and reported that LightGBM and ANN have proven stable and accurate in determining daily RET. It is worth mentioning that the ML models' performance in daily RET estimation is influenced by various factors, including the selection of input climatic variables, model structure, basic parameters, and performance criteria.…”

“…Careful consideration of these factors and the correlation between individual input variables and RET is crucial for optimizing the ML models' accuracy and efficiency. Additionally, effective tuning of ML model parameters further enhances their performance and efficiency for correct estimation [27][28][29][30][31][32][33][34].…”

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

“…In this model, the IOT framework using the dataset helps to analyse the attributes and control of remote sensing devices like humidity sensors, temperature sensors, and moisturizer sensors [14]. Decision trees and ANN combine to predict crops using meteorological attributes like air temperature, wind speed, and solar radiation [15,16]. Various approaches are used to solve the problem using DL algorithms, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and long-short-term memory (LSTM).…”