干旱区生态环境知识资源中心(Arid): A Machine Learning Framework for Olive Farms Profit Prediction

Arid

DOI	10.3390/w13233461
	A Machine Learning Framework for Olive Farms Profit Prediction
	Christias, Panagiotis; Mocanu, Mariana
通讯作者	Christias, P (corresponding author), Univ Politehn Bucuresti, Fac Automat Control & Comp, Bucharest 060042, Romania.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:23
英文摘要	Agricultural systems are constantly stressed due to higher demands for products. Consequently, water resources consumed on irrigation are increased. In combination with the climatic change, those are major obstacles to maintaining sustainable development, especially in a semi-arid land. This paper presents an end-to-end Machine Learning framework for predicting the potential profit from olive farms. The objective is to estimate the optimal economic gain while preserving water resources on irrigation by considering various related factors such as climatic conditions, crop management practices, soil characteristics, and crop yield. The case study focuses on olive tree farms located on the Hellenic Island of Crete. Real data from the farms and the weather in the area will be used. The target is to build a framework that will preprocess input data, compare the results among a group of Machine Learning algorithms and propose the best-predicted value of economic profit. Various aspects during this process will be thoroughly examined such as the bias-variance tradeoff and the problem of overfitting, data transforms, feature engineering and selection, ensemble methods as well as pursuing optimal resampling towards better model accuracy. Results indicated that through data preprocessing and resampling, Machine Learning algorithms performance is enhanced. Ultimately, prediction accuracy and reliability are greatly improved compared to algorithms' performances without the framework's operation.
英文关键词	Machine Learning predictive modeling data preprocessing resampling ensemble methods irrigation management olive tree farms
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000735091900001
WOS关键词	DECISION-SUPPORT-SYSTEMS ; CROSS-VALIDATION ; WATER PRODUCTIVITY ; IRRIGATION ; YIELD ; REGRESSION ; MODEL ; COEFFICIENT ; CHALLENGES ; NETWORKS
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373932
作者单位	[Christias, Panagiotis; Mocanu, Mariana] Univ Politehn Bucuresti, Fac Automat Control & Comp, Bucharest 060042, Romania
推荐引用方式 GB/T 7714	Christias, Panagiotis,Mocanu, Mariana. A Machine Learning Framework for Olive Farms Profit Prediction[J],2021,13(23).
APA	Christias, Panagiotis,&Mocanu, Mariana.(2021).A Machine Learning Framework for Olive Farms Profit Prediction.WATER,13(23).
MLA	Christias, Panagiotis,et al."A Machine Learning Framework for Olive Farms Profit Prediction".WATER 13.23(2021).