{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:08:51Z","timestamp":1742929731874,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031383434"},{"type":"electronic","value":"9783031383441"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-38344-1_6","type":"book-chapter","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T08:05:11Z","timestamp":1689926711000},"page":"50-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for\u00a0Automatic Weather Stations: A Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1229-8807","authenticated-orcid":false,"given":"Rog\u00e9rio P.","family":"dos Santos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7315-8739","authenticated-orcid":false,"given":"Marko","family":"Beko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-9271","authenticated-orcid":false,"given":"Valderi Reis Quietinho","family":"Leithardt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2022.106401","volume":"279","author":"F Molero","year":"2022","unstructured":"Molero, F., Barrag\u00e1n, R., Art\u00ed\u00f1ano, B.: Estimation of the atmospheric boundary layer height by means of machine learning techniques using ground-level meteorological data. Atmos. Res. 279, 106401 (2022). https:\/\/doi.org\/10.1016\/j.atmosres.2022.106401","journal-title":"Atmos. Res."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","volume":"338","author":"Y Feng","year":"2019","unstructured":"Feng, Y., Cui, N., Hao, W., Gao, L., Gong, D.: Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338, 67\u201377 (2019). https:\/\/doi.org\/10.1016\/j.geoderma.2018.11.044","journal-title":"Geoderma"},{"issue":"16","key":"6_CR3","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.3390\/rs13163101","volume":"13","author":"EH Bouras","year":"2021","unstructured":"Bouras, E.H., et al.: Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco. Remote Sens. 13(16), 3101 (2021). https:\/\/doi.org\/10.3390\/rs13163101","journal-title":"Remote Sens."},{"key":"6_CR4","series-title":"Algorithms for Intelligent Systems","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/978-981-15-8530-2_58","volume-title":"Data Intelligence and Cognitive Informatics","author":"SA Shetty","year":"2021","unstructured":"Shetty, S.A., Padmashree, T., Sagar, B.M., Cauvery, N.K.: Performance analysis on machine learning algorithms with deep learning model for crop yield prediction. In: Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S., Falkowski-Gilski, P. (eds.) Data Intelligence and Cognitive Informatics. AIS, pp. 739\u2013750. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-8530-2_58"},{"issue":"4","key":"6_CR5","doi-asserted-by":"publisher","first-page":"140","DOI":"10.38094\/jastt1457","volume":"1","author":"D Maulud","year":"2020","unstructured":"Maulud, D., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140\u2013147 (2020). https:\/\/doi.org\/10.38094\/jastt1457","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Khosravi, K., et al.: Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Comput. Electron. Agric. 167, 105041 (2019). https:\/\/doi.org\/10.1016\/j.compag.2019.105041. ISSN 0168-1699","DOI":"10.1016\/j.compag.2019.105041"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"Alizamir, M., Kim, S., Kisi, O., Zounemat-Kermani, M.: A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy 197, 117239 (2020). https:\/\/doi.org\/10.1016\/j.energy.2020.117239. ISSN 0360-5442","DOI":"10.1016\/j.energy.2020.117239"},{"issue":"5","key":"6_CR8","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s13351-019-8162-6","volume":"33","author":"K Zhou","year":"2019","unstructured":"Zhou, K., Zheng, Y., Li, B., Dong, W., Zhang, X.: Forecasting different types of convective weather: a deep learning approach. J. Meteorol. Res. 33(5), 797\u2013809 (2019). https:\/\/doi.org\/10.1007\/s13351-019-8162-6","journal-title":"J. Meteorol. Res."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Ko, J., Lee, K., Hwang, H., Shin, K.: Deep-learning-based precipitation nowcasting with ground weather station data and radar data. arXiv preprint arXiv:2210.12853 (2022)","DOI":"10.1109\/ICDMW58026.2022.00138"},{"key":"6_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.deveng.2018.100040","volume":"4","author":"M Nsabagwa","year":"2019","unstructured":"Nsabagwa, M., Byamukama, M., Kondela, E., Otim, J.S.: Towards a robust and affordable automatic weather station, development. Engineering 4, 100040 (2019). https:\/\/doi.org\/10.1016\/j.deveng.2018.100040","journal-title":"Engineering"},{"issue":"4","key":"6_CR11","doi-asserted-by":"publisher","first-page":"146","DOI":"10.3390\/info12040146","volume":"12","author":"K Ioannou","year":"2021","unstructured":"Ioannou, K., Karampatzakis, D., Amanatidis, P., Aggelopoulos, V., Karmiris, I.: Low-cost automatic weather stations in the internet of things. Information 12(4), 146 (2021). https:\/\/doi.org\/10.3390\/info12040146","journal-title":"Information"},{"issue":"8","key":"6_CR12","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.5194\/essd-13-3819-2021","volume":"13","author":"RS Fausto","year":"2021","unstructured":"Fausto, R.S., et al.: Programme for monitoring of the Greenland Ice sheet (PROMICE) automatic weather station data. Earth Syst. Sci. Data 13(8), 3819\u20133845 (2021). https:\/\/doi.org\/10.5194\/essd-13-3819-2021","journal-title":"Earth Syst. Sci. Data"},{"key":"6_CR13","doi-asserted-by":"publisher","unstructured":"Munandar, A., Fakhrurroja, H., Rizqyawan, M.I., Pratama, R.P., Wibowo, J.W., Anto, I.A.F.: Design of real-time weather monitoring system based on mobile application using automatic weather station. In: 2017 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Jakarta, Indonesia, pp. 44\u201347 (2017). https:\/\/doi.org\/10.1109\/ICACOMIT.2017.8253384","DOI":"10.1109\/ICACOMIT.2017.8253384"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Schultz, M.G., et al.: Can deep learning beat numerical weather prediction? Philos. Trans. R. Soc. A 379(2194), 20200097 (2021). https:\/\/doi.org\/10.1098\/rsta.2020.0097","DOI":"10.1098\/rsta.2020.0097"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Matilla, D.M., et al.: Low-cost edge computing devices and novel user interfaces for monitoring pivot irrigation systems based on Internet of Things and LoRaWAN technologies. J. Biosyst. Eng. (2022). https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.07.010","DOI":"10.1016\/j.biosystemseng.2021.07.010"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Dos Santos, R.P., Beko, M., Leithardt, V.R.: Package proposal for data pre-processing for machine learning applied to precision irrigation. In: 6th Conference on Cloud and Internet of Things (CIoT) (2023). https:\/\/doi.org\/10.1109\/CIoT57267.2023.10084899","DOI":"10.1109\/CIoT57267.2023.10084899"},{"key":"6_CR17","doi-asserted-by":"publisher","unstructured":"dos Santos, R.P., Fachada, N., Beko, M., Leithardt, V.R.Q.: A rapid review on the use of free and open source technologies and software applied to precision agriculture practices. J. Sens. Actuator Netw. https:\/\/doi.org\/10.3390\/jsan12020028","DOI":"10.3390\/jsan12020028"},{"key":"6_CR18","doi-asserted-by":"publisher","unstructured":"Dos Santos, R.P., Leithardt, V.R.Q., Beko, M.: Analysis of MQTT-SN and LWM2M communication protocols for precision agriculture IoT devices. In: 17th (CISTI) (2022) .https:\/\/doi.org\/10.23919\/CISTI54924.2022.9820048","DOI":"10.23919\/CISTI54924.2022.9820048"}],"container-title":["Advances in Intelligent Systems and Computing","New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-38344-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T08:06:20Z","timestamp":1689926780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-38344-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031383434","9783031383441"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-38344-1_6","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DiTTEt","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2nd","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dittet2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dittet.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}