Research Article: Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration

Date Published: May 31, 2019

Publisher: Public Library of Science

Author(s): Lifeng Wu, Junliang Fan, Paweł Pławiak.

http://doi.org/10.1371/journal.pone.0217520

Abstract

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models.

Partial Text

Accurate prediction of reference evapotranspiration (ET0) is significant for irrigation schedules design, crop growth modeling and agricultural water management [1–5]. Various mathematical models have been proposed to estimate ET0 from meteorological variables, among which the FAO-56 Penman–Monteith (FAO-56 PM) equation is suggested by the Food and Agriculture Organization of the United Nations as a reference model in various regions and climates [6], because it considerers both the thermodynamic and aerodynamic items. However, the FAO-56 PM model needs a variety of climatic parameters as model inputs for calculation, e.g., maximum and minimum ambient temperatures, wind speed, relative humidity and net radiation [7–11], which significantly restricts the application of the FAO-56 PM model in many worldwide regions. Therefore, the simplified empirical models with fewer climatic variables is becoming increasingly popular in the absence of compete data [12–15], such as temperature-based models [16], mass transfer-based models [17] and radiation-based models [18]. However, evapotranspiration is a complex and highly nonlinear phenomenon dependent on several climatic parameters. Therefore, it is difficult to establish empirical models that can consider all those complicated processes. In recent years, much attention has been drawn to use alternative techniques such as machine learning models for ET0 prediction as a result of their excellent performance in tackling the nonlinear relationship between the model inputs and output [19–21].

The performance of eight machine learning models in four categories, e.g. neuron-based (MLP, GRNN, ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for the estimation of daily ET0 were compared based on only temperature and precipitation data during 2001–2015 obtained from 14 representative stations across various climatic zones of China. The results showed that the machine learning models using only temperature attained satisfactory daily ET0 estimation. The prediction accuracy was further improved across China when information of precipitation was considered, especially in the (sub)tropical humid regions. This indicates that precipitation is a manifestation of relative humidity to some extent and can correct the temperature-based ET0 models. The kernel-based SVM, KNEA and curve-based MARS models generally gave more accurate daily ET0 estimates than the other models for, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, as well as a similar best performance by them in NEC and NC. The SVM, MLP, MARS and KNEA models showed relatively small percentage increase in RMSE during testing over the training one. Comprehensively considering both prediction accuracy and model stability, SVM is highly suggested, while KNEA and MARS are also alternative models for predicting daily ET0 in various climatic regions of China. The satisfactory performances of these proposed machine learning models with ambient temperatures and transformed precipitation indicates that it is possible for near-future prediction of daily ET0 using public weather forecasts, including daily maximum and minimum temperatures and whether there is precipitation or not. Nevertheless, more study is needed to explore the performances of the proposed machine learning models at varying temporal scales or in various climatic regions.

 

Source:

http://doi.org/10.1371/journal.pone.0217520

 

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