{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:45:51Z","timestamp":1760233551860,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T00:00:00Z","timestamp":1611360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1307504"],"award-info":[{"award-number":["2018YFB1307504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station\u2019s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.<\/jats:p>","DOI":"10.3390\/s21030755","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T09:59:40Z","timestamp":1611568780000},"page":"755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica"],"prefix":"10.3390","volume":"21","author":[{"given":"Yuchen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinke","family":"Dou","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangxiao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxue","family":"Guo","sequence":"additional","affiliation":[{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomin","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghu","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather and Institute of Tibetan Plateau &amp; Polar Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyuan","family":"Tang","sequence":"additional","affiliation":[{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1175\/WAF-D-18-0171.1","article-title":"Antarctic Verification of the Australian Numerical Weather Pre-diction Model","volume":"34","author":"Schroeter","year":"2019","journal-title":"Weather. Forecast."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1016\/j.oceaneng.2009.08.008","article-title":"Comparison between M5\u2032 model tree and neural networks for prediction of significant wave height in Lake Superior","volume":"36","author":"Mahjoobi","year":"2009","journal-title":"Ocean Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115682","DOI":"10.1016\/j.image.2019.115682","article-title":"Novel calibration method for camera array in spherical arrangement","volume":"80","author":"An","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yeh, C.-H., Lin, M.-H., Lin, C.-H., Yu, C.-E., and Chen, M.-J. (2019). Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine. Sensors, 19.","DOI":"10.3390\/s19071671"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sanchez-Medina, J.J., Guerra-Montenegro, J., Sanchez-Rodriguez, D., Alonso-Gonz\u00e1lez, I., and Navarro-Mesa, J.L. (2019). Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands. Sensors, 19.","DOI":"10.3390\/s19102388"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Salazar, L.R., Cobano, J.A., and Ollero, A. (2016). Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors, 17.","DOI":"10.3390\/s17010008"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mei, B., Sun, L., and Shi, G. (2020). Full-Scale Maneuvering Trials Correction and Motion Modelling Based on Actual Sea and Weather Conditions. Sensors, 20.","DOI":"10.3390\/s20143963"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bilgera, C., Yamamoto, A., Sawano, M., Matsukura, H., and Ishida, H. (2018). Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Out-door Environments. Sensors, 18.","DOI":"10.3390\/s18124484"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1002\/joc.1143","article-title":"Towards ice-core-based synoptic reconstructions of west antarctic climate with artificial neural networks","volume":"25","author":"Reusch","year":"2005","journal-title":"Int. J. Clim."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1029\/2003JD004178","article-title":"A 15-year West Antarctic climatology from six automatic weather station temperature and pressure records","volume":"109","author":"Reusch","year":"2004","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107424","DOI":"10.1016\/j.oceaneng.2020.107424","article-title":"Forecasting, hindcasting and feature selection of ocean waves via recurrent and se-quence-to-sequence networks","volume":"207","author":"Pirhooshyaran","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ins.2020.03.018","article-title":"Stacked isomorphic autoencoder based soft analyzer and its applica-tion to sulfur recovery unit","volume":"534","author":"Yuan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.ins.2019.11.039","article-title":"Deep relevant representation learning for soft sensing","volume":"514","author":"Yan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1109\/TII.2019.2938890","article-title":"Hierarchical Quality-Relevant Feature Represen-tation for Soft Sensor Modeling: A Novel Deep Learning Strategy","volume":"16","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172816","DOI":"10.1109\/ACCESS.2019.2955957","article-title":"A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean","volume":"7","author":"Han","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.energy.2018.05.052","article-title":"Effective long short-term memory with differential evolution algorithm for electricity price prediction","volume":"162","author":"Peng","year":"2018","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107298","DOI":"10.1016\/j.oceaneng.2020.107298","article-title":"A novel model to predict significant wave height based on long short-term memory network","volume":"205","author":"Fan","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.energy.2019.116704","article-title":"Forecast the electricity price of U.S. using a wavelet transform-based hybrid model","volume":"193","author":"Qiao","year":"2020","journal-title":"Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.csl.2018.06.005","article-title":"A Bi-LSTM memory network for end-to-end goal-oriented dialog learning","volume":"53","author":"Kim","year":"2019","journal-title":"Comput. Speech Lang."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v067.i01","article-title":"Fitting Linear Mixed-Effects Models Using lme4","volume":"67","author":"Bates","year":"2015","journal-title":"J. Stat. Softw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/01621459.1993.10594284","article-title":"Approximate inference in generalized linear mixed models","volume":"88","author":"Breslow","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:14:28Z","timestamp":1760159668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,23]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030755"],"URL":"https:\/\/doi.org\/10.3390\/s21030755","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,23]]}}}