{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T10:40:57Z","timestamp":1776163257531,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:00:00Z","timestamp":1693612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFF0711700"],"award-info":[{"award-number":["2022YFF0711700"]}]},{"name":"National Key R&amp;D Program of China","award":["H1814cc012"],"award-info":[{"award-number":["H1814cc012"]}]},{"name":"National Key R&amp;D Program of China","award":["E2297801"],"award-info":[{"award-number":["E2297801"]}]},{"name":"School of Computer and Communication, Lanzhou University of Technology","award":["2022YFF0711700"],"award-info":[{"award-number":["2022YFF0711700"]}]},{"name":"School of Computer and Communication, Lanzhou University of Technology","award":["H1814cc012"],"award-info":[{"award-number":["H1814cc012"]}]},{"name":"School of Computer and Communication, Lanzhou University of Technology","award":["E2297801"],"award-info":[{"award-number":["E2297801"]}]},{"name":"Light of West China Program of Chinese Academy of Sciences","award":["2022YFF0711700"],"award-info":[{"award-number":["2022YFF0711700"]}]},{"name":"Light of West China Program of Chinese Academy of Sciences","award":["H1814cc012"],"award-info":[{"award-number":["H1814cc012"]}]},{"name":"Light of West China Program of Chinese Academy of Sciences","award":["E2297801"],"award-info":[{"award-number":["E2297801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.<\/jats:p>","DOI":"10.3390\/a16090422","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:43:20Z","timestamp":1693795400000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Cong","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Communication, LanZhou University of Technology, LanZhou 730050, China"},{"name":"National Cryosphere Desert Date Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Xupeng","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, LanZhou University of Technology, LanZhou 730050, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2064-8616","authenticated-orcid":false,"given":"Guohui","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Cryosphere Desert Date Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,2]]},"reference":[{"key":"ref_1","first-page":"1247","article-title":"Big Data Analytics in Weather Forecasting: A Systematic Review","volume":"5","author":"Fathi","year":"2021","journal-title":"Arch. 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