Research on filling methods of missing data in cultivated land quality evaluation

In the process of cultivated land quality data investigation and collection, there will be missing data due to human, environmental, and other factors. However, the current missing data-filling methods have insufficient applicability. In order to improve the cultivated land quality database and eval...

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Autores principales:	CHEN Yu, ZHOU Wu, HU Yueming, XIE Jianwen
Formato:	article
Lenguaje:	ZH
Publicado:	Agro-Environmental Protection Institute, Ministry of Agriculture 2021
Materias:	evaluation of cultivated land quality missing data filling conghua district accuracy Agriculture (General) S1-972 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/349a7eaa1425435c846571df3a02b94a
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id	oai:doaj.org-article:349a7eaa1425435c846571df3a02b94a
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spelling	oai:doaj.org-article:349a7eaa1425435c846571df3a02b94a2021-12-03T02:29:42ZResearch on filling methods of missing data in cultivated land quality evaluation2095-681910.13254/j.jare.2021.0201https://doaj.org/article/349a7eaa1425435c846571df3a02b94a2021-11-01T00:00:00Zhttp://www.aed.org.cn/nyzyyhjxb/html/2021/6/20210620.htmhttps://doaj.org/toc/2095-6819In the process of cultivated land quality data investigation and collection, there will be missing data due to human, environmental, and other factors. However, the current missing data-filling methods have insufficient applicability. In order to improve the cultivated land quality database and evaluation accuracy, it is important to explore missing data-filling methods in cultivated land quality evaluation. In this study, the cultivated land quality database of Conghua District Guangzhou City was used as the sample set. According to the spatial correlation and spatial distribution, the dataset was divided into spatial and non-spatial correlation datasets. Various filling methods were used to simulate the missing data filling, and a cross method was used to verify the accuracy. The results indicated the proportion of total outliers was less than 1.2%, and 25 factors such as elevation, temperature, and available zinc showed spatial correlation. The four-image nearest neighbor algorithm presented the highest filling accuracy for spatial association data, and the accuracy was as high as 80% when the missing rate was less than 20%. The accuracy decreased with the increase in the missing rate. The four-image nearest neighbor algorithm was followed by K-nearest neighbor algorithm(KNN), expectation maximization algorithm, multiple interpolation algorithm, and regression model algorithm. The four-image nearest neighbor algorithm showed better accuracy than K-nearest neighbor algorithm when the data was dense. For the non-spatial correlation dataset, the highest filling accuracy was the similar aggregation filling algorithm, which could maintain more than 80% accuracy within 25% of the missing rate, followed by expectation maximization algorithm, multiple interpolation algorithm, and regression model algorithm. To sum up, the four-image nearest neighbor algorithm and the similar aggregation filling algorithm proposed in this study show higher accuracy, more stable effect, and wider practicability than other algorithms for filling missing data in cultivated land quality evaluation.CHEN YuZHOU WuHU YuemingXIE JianwenAgro-Environmental Protection Institute, Ministry of Agriculturearticleevaluation of cultivated land qualitymissingdatafillingconghua districtaccuracyAgriculture (General)S1-972Environmental sciencesGE1-350ZHJournal of Agricultural Resources and Environment, Vol 38, Iss 6, Pp 1132-1141 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	evaluation of cultivated land quality missing data filling conghua district accuracy Agriculture (General) S1-972 Environmental sciences GE1-350
spellingShingle	evaluation of cultivated land quality missing data filling conghua district accuracy Agriculture (General) S1-972 Environmental sciences GE1-350 CHEN Yu ZHOU Wu HU Yueming XIE Jianwen Research on filling methods of missing data in cultivated land quality evaluation
description	In the process of cultivated land quality data investigation and collection, there will be missing data due to human, environmental, and other factors. However, the current missing data-filling methods have insufficient applicability. In order to improve the cultivated land quality database and evaluation accuracy, it is important to explore missing data-filling methods in cultivated land quality evaluation. In this study, the cultivated land quality database of Conghua District Guangzhou City was used as the sample set. According to the spatial correlation and spatial distribution, the dataset was divided into spatial and non-spatial correlation datasets. Various filling methods were used to simulate the missing data filling, and a cross method was used to verify the accuracy. The results indicated the proportion of total outliers was less than 1.2%, and 25 factors such as elevation, temperature, and available zinc showed spatial correlation. The four-image nearest neighbor algorithm presented the highest filling accuracy for spatial association data, and the accuracy was as high as 80% when the missing rate was less than 20%. The accuracy decreased with the increase in the missing rate. The four-image nearest neighbor algorithm was followed by K-nearest neighbor algorithm(KNN), expectation maximization algorithm, multiple interpolation algorithm, and regression model algorithm. The four-image nearest neighbor algorithm showed better accuracy than K-nearest neighbor algorithm when the data was dense. For the non-spatial correlation dataset, the highest filling accuracy was the similar aggregation filling algorithm, which could maintain more than 80% accuracy within 25% of the missing rate, followed by expectation maximization algorithm, multiple interpolation algorithm, and regression model algorithm. To sum up, the four-image nearest neighbor algorithm and the similar aggregation filling algorithm proposed in this study show higher accuracy, more stable effect, and wider practicability than other algorithms for filling missing data in cultivated land quality evaluation.
format	article
author	CHEN Yu ZHOU Wu HU Yueming XIE Jianwen
author_facet	CHEN Yu ZHOU Wu HU Yueming XIE Jianwen
author_sort	CHEN Yu
title	Research on filling methods of missing data in cultivated land quality evaluation
title_short	Research on filling methods of missing data in cultivated land quality evaluation
title_full	Research on filling methods of missing data in cultivated land quality evaluation
title_fullStr	Research on filling methods of missing data in cultivated land quality evaluation
title_full_unstemmed	Research on filling methods of missing data in cultivated land quality evaluation
title_sort	research on filling methods of missing data in cultivated land quality evaluation
publisher	Agro-Environmental Protection Institute, Ministry of Agriculture
publishDate	2021
url	https://doaj.org/article/349a7eaa1425435c846571df3a02b94a
work_keys_str_mv	AT chenyu researchonfillingmethodsofmissingdataincultivatedlandqualityevaluation AT zhouwu researchonfillingmethodsofmissingdataincultivatedlandqualityevaluation AT huyueming researchonfillingmethodsofmissingdataincultivatedlandqualityevaluation AT xiejianwen researchonfillingmethodsofmissingdataincultivatedlandqualityevaluation
_version_	1718373923457859584

Research on filling methods of missing data in cultivated land quality evaluation

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