干旱区生态环境知识资源中心(Arid): Machine learning-based cloud computing improved wheat yield simulation in arid regions

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DOI	10.1016/j.compag.2022.107457
	Machine learning-based cloud computing improved wheat yield simulation in arid regions
	Kheir, Ahmed M. S.; Ammar, Khalil A.; Amer, Ahmed; Ali, Marwa G. M.; Ding, Zheli; Elnashar, Abdelrazek
通讯作者	Kheir, AMS
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2022
卷号	203
英文摘要	Combining machine learning (ML) with dynamic models is recommended by recent research for creating a hybrid approach for robust simulations but has received less attention thus far. Herein, we combined multi- ML algorithms with multi-crop models (CMs) of the DSSAT platform to develop a hybrid approach for wheat yield simulation over 40 years in different locations. The simulation analysis included temperatures (minimum and maximum), solar radiation, and precipitation as important key ecological factors in wheat production that varied across sites and years. Detailed observed datasets of wheat yield from 1981 to 2020 were used for training and testing Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Support Vector Regressor (SVR) using Google Colaboratory (Colab). Such models were built to create four main approaches, including two approaches as hybrid (CMs-ML) and benchmark (pure ML), as well as two testing methods for each approach such as default (75 % training and 25 % testing) and warmest years (2001, 2006, 2009, 2010, and 2018). In addition to wheat yield simulations, ML approaches were used to identify the important features, improve accuracy, and reduce overfitting. We developed ML approaches by novel cells on the built models (i.e., pure ML and hybrid) to eliminate less important features from permutation. Our results revealed that ANN and RFR outperformed other ML algorithms (SVR and KNN) in wheat yield simulation accuracy. Application of ML algorithms reduced yield change from 31.7 % under DSSAT simulations to 8.1 % and uncertainty from 12.8 % to 7.2 % relative to observed wheat yield over the last four decades (1981-2020). Our novel approach, which includes a hybrid CMs-ML model, cloud computing, and a new permutation tool, could be effectively used for robust crop yield simulation on a regional and global scale, contributing to better aid decision-making strategies.
英文关键词	Wheat yield simulation DSSAT Artificial Neural Network Random Forest Support Vector Regressor K -Nearest Neighbors Google Colaboratory Uncertainty
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000882141100004
WOS关键词	CLIMATE-CHANGE IMPACT ; CERES-WHEAT ; MODEL ; PREDICTION ; UNCERTAINTY ; ADAPTATION ; EXPRESSION ; CHALLENGE ; GROWTH ; LEVEL
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392187
推荐引用方式 GB/T 7714	Kheir, Ahmed M. S.,Ammar, Khalil A.,Amer, Ahmed,et al. Machine learning-based cloud computing improved wheat yield simulation in arid regions[J],2022,203.
APA	Kheir, Ahmed M. S.,Ammar, Khalil A.,Amer, Ahmed,Ali, Marwa G. M.,Ding, Zheli,&Elnashar, Abdelrazek.(2022).Machine learning-based cloud computing improved wheat yield simulation in arid regions.COMPUTERS AND ELECTRONICS IN AGRICULTURE,203.
MLA	Kheir, Ahmed M. S.,et al."Machine learning-based cloud computing improved wheat yield simulation in arid regions".COMPUTERS AND ELECTRONICS IN AGRICULTURE 203(2022).