Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China

By Junliang Fan, Wenjun Yue, Lifeng Wu, Fucang Zhang, H. Cai, Xiukang Wang, Xianghui Lu, Youzhen Xiang. Agricultural and Forest Meteorology, 2018

462 citations recorded by the source corpus.

Abstract Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-Monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (i.e. random forest (RF), M5 model tree (M5Tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVM) and extreme learning machine (ELM) models. Four input combinations of daily maximum and maximum temperature (Tmax and Tmin), relative humidity (Hr), wind speed (U2), global and extra-terrestrial solar radiation (Rs and Ra) with Tmax, Tmin and Ra as the base dataset were considered using meteorological data during 1961–2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using Tmax, Tmin, Hr, U2 and Ra obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RMSE

View Paper: https://www.semanticscholar.org/paper/bc3287dbcbe1c4c56d62bcac31dc68bf8cf8be9c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mohammad Javad Taheri Amiri
2. Parisa Masoumi
3. Mahsa Asadi-shiadeh
4. Mojtaba Yaghoubi-chenari
5. Narjes Nabipour
6. Amir Mosavi
7. Alireza Ashrafian

Citations in the corpus

• Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, background): “become increasingly implemented for regression challenges because they are relatively simple, straightforward, flexible and have relatively low-cost computational process [24].”