A hybrid optimized model for predicting evapotranspiration in early and late rice based on a categorical regression tree combination of key influencing factors

•The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The SSA-LSSVM model performs best among the optimization models. Crop evapotranspiration (ETC) is an important component of the agricultural hydrolog...

Full description

Saved in:
Bibliographic Details
Published in:Computers and electronics in agriculture Vol. 211; p. 108031
Main Authors: Zhao, Long, Qing, Shunhao, Bai, Jiayi, Hao, Haohao, Li, Hui, Shi, Yi, Xing, Xuguang, Yang, Ru
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2023
Subjects:
agriculture
Categorical Regression Tree
electronics
Evapotranspiration
hybrids
hydrologic cycle
irrigation
leaf area index
Least Squares Support Vector Machine
Meta-heuristic optimization algorithms
prediction
regression analysis
Rice
soil water
solar radiation
support vector machines
temperature
wind speed
Least Squares Support Vector Machine
Categorical Regression Tree
Meta-heuristic optimization algorithms
Evapotranspiration
Rice
ISSN:0168-1699, 1872-7107
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The SSA-LSSVM model performs best among the optimization models. Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the hydrological environment of farmlands and guiding crop irrigation. To improve the prediction accuracy of ETC for early and late rice, this study analyzed the importance of the influencing factors based on a categorical regression tree (CART) machine learning algorithm. Based on the importance results of the influencing factors obtained from CART analysis, different combinations of input factors were constructed, and daily ETC prediction models were developed for early and late rice using the least squares support vector machine (LSSVM). A three-element heuristic optimization algorithm, Sine Cosine Algorithm (SCA), Slime Mold Algorithm (SMA), and Sparrow Search Algorithm (SSA), were used to optimize the ETC model to improve the performance of the early and late rice ETC prediction models. The results of this study revealed that the strongest correlation between early and late rice ETC was net solar radiation (Rn), with importance of 0.830 and 0.802, respectively. The next strongest correlations were temperature (T), wind speed (u), soil moisture (SM) and u, leaf area index (LAI), SM (0.092, 0.02, 0.014 and 0.058, 0.049, 0.034), respectively. The accuracy of the model tended to increase as the number of influencing factors in the input combinations constructed based on the importance results increased. The accuracy of the model increased more rapidly when the input factors were one to four and the accuracy of the model increased slightly when the input factors were more than four. The SSA-LSSVM model with four-factor combinations of inputs was the least-factor combination with guaranteed model accuracy of RMSE = 0.514 mm/d, R2 = 0.924, KGE = 0.565, WI = 0.978,and MAE = 0.414 mm/d; RMSE = 0.468 mm/d, R2 = 0.910, KGE = 0.670, WI = 0.985, and MAE = 0.368 mm/d. This study may provide a reference for the prediction of evapotranspiration as a key factor in the study of the agricultural hydrological environment of early and late rice.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2023.108031