{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:20:29Z","timestamp":1743106829554,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031755392"},{"type":"electronic","value":"9783031755408"}],"license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-75540-8_5","type":"book-chapter","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:03:34Z","timestamp":1729119814000},"page":"54-67","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A ConvLSTM Approach for the WorldClim Dataset in Mexico"],"prefix":"10.1007","author":[{"given":"Jorge Gerardo","family":"Iglesias-Ortiz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adri\u00e1n Isa\u00ed","family":"Morales-Paredes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas Antonio Low-Castro Miguel","family":"Gonz\u00e1lez-Mendoza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilberto","family":"Ochoa-Ruiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"issue":"3\u20134","key":"5_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10584-021-03122-z","volume":"166","author":"SB Ajjur","year":"2021","unstructured":"Ajjur, S.B., Al-Ghamdi, S.G.: Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Clim. Change 166(3\u20134), 28 (2021)","journal-title":"Clim. Change"},{"issue":"11","key":"5_CR2","first-page":"7","volume":"143","author":"M Akram","year":"2016","unstructured":"Akram, M., El, C.: Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int. J. Comput. Appl. 143(11), 7\u201311 (2016)","journal-title":"Int. J. Comput. Appl."},{"issue":"16","key":"5_CR3","doi-asserted-by":"publisher","first-page":"3890","DOI":"10.3390\/rs14163890","volume":"14","author":"AI Albu","year":"2022","unstructured":"Albu, A.I., Czibula, G., Mihai, A., Czibula, I.G., Burcea, S., Mezghani, A.: Nextnow: a convolutional deep learning model for the prediction of weather radar data for nowcasting purposes. Remote Sens. 14(16), 3890 (2022)","journal-title":"Remote Sens."},{"key":"5_CR4","first-page":"1","volume":"7","author":"M Andrade-Vel\u00e1zquez","year":"2020","unstructured":"Andrade-Vel\u00e1zquez, M., Perez, O.R.M.: Precipitation patterns in Usumacinta and Grijalva basins (southern Mexico) under a changing climate. Revista Bio Ciencias 7, 1\u201322 (2020)","journal-title":"Revista Bio Ciencias"},{"issue":"1","key":"5_CR5","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s42055-020-00031-4","volume":"3","author":"FI Arreguin-Cortes","year":"2020","unstructured":"Arreguin-Cortes, F.I., et al.: State level water security indices in Mexico. Sustainable Earth 3(1), 9 (2020)","journal-title":"Sustainable Earth"},{"issue":"4","key":"5_CR6","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.3390\/ijerph18041846","volume":"18","author":"J Bravo-Cadena","year":"2021","unstructured":"Bravo-Cadena, J., Pav\u00f3n, N.P., Balvanera, P., S\u00e1nchez-Rojas, G., Razo-Zarate, R.: Water availability-demand balance under climate change scenarios in an overpopulated region of Mexico. Int. J. Environ. Res. Public Health 18(4), 1846 (2021)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"5_CR7","unstructured":"Laura, C.: Planning Approaches to the Management of Water Problems in Mexico. In Maria Pomffyova, editor, Process Management. InTech, April (2010)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, A., Mustafa, M., Hassanzadeh, P., Bach, E., Kashinath, K.. Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving spatial transformers in a case study with ERA5, (2021)","DOI":"10.5194\/gmd-2021-71"},{"issue":"2","key":"5_CR9","doi-asserted-by":"publisher","first-page":"A150819","DOI":"10.18268\/BSGM2020v72n2a150819","volume":"72","author":"C Deng","year":"2020","unstructured":"Deng, C., Pisani, B., Hern\u00e1ndez, H., Li, Y.: Assessing the impact of climate change on water resources in a semi-arid area in central Mexico using a SWAT model. Bolet\u00edn de la Sociedad Geol\u00f3gica Mexicana 72(2), A150819 (2020)","journal-title":"Bolet\u00edn de la Sociedad Geol\u00f3gica Mexicana"},{"issue":"2","key":"5_CR10","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1175\/BAMS-D-15-00070.1","volume":"97","author":"DR Durran","year":"2016","unstructured":"Durran, D.R., Weyn, J.A.: Thunderstorms do not get butterflies. Bull. Am. Meteorol. Soc. 97(2), 237\u2013243 (2016)","journal-title":"Bull. Am. Meteorol. Soc."},{"issue":"12","key":"5_CR11","doi-asserted-by":"publisher","first-page":"4302","DOI":"10.1002\/joc.5086","volume":"37","author":"SE Fick","year":"2017","unstructured":"Fick, S.E., Hijmans, R.J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302\u20134315 (2017)","journal-title":"Int. J. Climatol."},{"key":"5_CR12","first-page":"57","volume":"376","author":"J Godinez Madrigal","year":"2018","unstructured":"Godinez Madrigal, J., Van Der Zaag, P., Van Cauwenbergh, N.: A half-baked solution: drivers of water crises in Mexico. Proc. Int. Assoc. Hydrol. Sci. 376, 57\u201362 (2018)","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"issue":"23","key":"5_CR13","doi-asserted-by":"publisher","first-page":"8931","DOI":"10.5194\/gmd-15-8931-2022","volume":"15","author":"B Gong","year":"2022","unstructured":"Gong, B., Langguth, M., Ji, Y., Mozaffari, A., Stadtler, S., Mache, K., Schultz, M.G.: Temperature forecasting by deep learning methods. Geosci. Model Dev. 15(23), 8931\u20138956 (2022)","journal-title":"Geosci. Model Dev."},{"issue":"4","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.5194\/esd-12-1503-2021","volume":"12","author":"HM Goulart","year":"2021","unstructured":"Goulart, H.M., Van Der Wiel, K., Folberth, C., Balkovic, J., Van Den Hurk, B.: Storylines of weather-induced crop failure events under climate change. Earth Syst. Dyn. 12(4), 1503\u20131527 (2021)","journal-title":"Earth Syst. Dyn."},{"issue":"1","key":"5_CR15","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1038\/s41597-020-0453-3","volume":"7","author":"I Harris","year":"2020","unstructured":"Harris, I., Osborn, T.J., Jones, P., Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7(1), 109 (2020)","journal-title":"Sci. Data"},{"issue":"1","key":"5_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s00704-023-04592-0","volume":"155","author":"J He","year":"2024","unstructured":"He, J., Liu, X., Wang, H., Zhu, D., Liu, Z.: A high-precision prediction method for coarse grids based on deep learning and the weather research and forecasting model. Theor. Appl. Climatol. 155(1), 117\u2013129 (2024)","journal-title":"Theor. Appl. Climatol."},{"issue":"21","key":"5_CR17","doi-asserted-by":"publisher","first-page":"16453","DOI":"10.1007\/s00500-020-04954-0","volume":"24","author":"P Hewage","year":"2020","unstructured":"Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F., Liu, Y.: Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station. Soft. Comput. 24(21), 16453\u201316482 (2020)","journal-title":"Soft. Comput."},{"issue":"11","key":"5_CR18","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1175\/BAMS-88-11-1783","volume":"88","author":"C Hohenegger","year":"2007","unstructured":"Hohenegger, C., Schar, C.: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Am. Meteorol. Soc. 88(11), 1783\u20131794 (2007)","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"5_CR19","unstructured":"Keras. Keras documentation: Next-Frame Video Prediction with Convolutional LSTMs"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Li, C., Zhao, M., Liu, Y., Xu, F.: Air temperature forecasting using traditional and deep learning algorithms. In: 2020 7th International conference on information science and control engineering (ICISCE) (pp. 189-194). IEEE (2020)","DOI":"10.1109\/ICISCE50968.2020.00049"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, Y., Zhao, G.: Deep learning with long short-term memory networks for air temperature predictions. In: 2019 International conference on artificial intelligence and advanced manufacturing (AIAM) (pp. 243-249). IEEE (2019).","DOI":"10.1109\/AIAM48774.2019.00056"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Guillermo\u00a0N. Murray-Tortarolo. Seven decades of climate change across Mexico. Atm\u00f3sfera, (2021)","DOI":"10.20937\/ATM.52803"},{"issue":"46","key":"5_CR23","first-page":"125","volume":"23","author":"J de Jes\u00fas","year":"2017","unstructured":"de Jes\u00fas, J., Ch\u00e1idez, N.: Water scarcity and degradation in the Rio San Juan watershed of Northeastern Mexico. Frontera Norte 23(46), 125\u2013150 (2017)","journal-title":"Frontera Norte"},{"issue":"2","key":"5_CR24","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1002\/2017MS000999","volume":"9","author":"T Palmer","year":"2017","unstructured":"Palmer, T.: The primacy of doubt: evolution of numerical weather prediction from determinism to probability. J. Adv. Model. Earth Syst. 9(2), 730\u2013734 (2017)","journal-title":"J. Adv. Model. Earth Syst."},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"de Paz, F. J.P., Granados, L.E.: El trasvase como dispositivo de la desigualdad h\u00eddrica persistente en M\u00e9xico, regi\u00f3n y sociedad, 33:e1439 (2021)","DOI":"10.22198\/rys2021\/33\/1439"},{"issue":"2","key":"5_CR26","doi-asserted-by":"publisher","first-page":"798","DOI":"10.3390\/s23020798","volume":"23","author":"M Samo","year":"2023","unstructured":"Samo, M., Mase, J.M.M., Figueredo, G.: Deep learning with attention mechanisms for road weather detection. Sensors 23(2), 798 (2023)","journal-title":"Sensors"},{"key":"5_CR27","unstructured":"Scientific Data Curation Team. Metadata record for: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, 2020. Artwork Size: 6330 Bytes Pages: 6330 Bytes"},{"key":"5_CR28","first-page":"802","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst. 28, 802 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"5_CR29","doi-asserted-by":"publisher","first-page":"11107","DOI":"10.1007\/s13762-023-05092-4","volume":"20","author":"A Utku","year":"2023","unstructured":"Utku, A., Can, U.: An efficient hybrid weather prediction model based on deep learning. Int. J. Environ. Sci. Technol. 20(10), 11107\u201311120 (2023)","journal-title":"Int. J. Environ. Sci. Technol."},{"issue":"11","key":"5_CR30","doi-asserted-by":"publisher","first-page":"e2021GL093777","DOI":"10.1029\/2021GL093777","volume":"48","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Chen, Z., Zhou, T.: Human influence on the increasing drought risk over Southeast Asian Monsoon Region. Geophys. Res. Lett. 48(11), e2021GL093777 (2021)","journal-title":"Geophys. Res. Lett."},{"issue":"11","key":"5_CR31","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1007\/s00376-023-3171-x","volume":"40","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Xiaojing, Yu., Zhou, T., Zhang, W., Shuai, H., Clark, R.: Understanding and attribution of extreme heat and drought events in 2022: current situation and future challenges. Adv. Atmos. Sci. 40(11), 1941\u20131951 (2023)","journal-title":"Adv. Atmos. Sci."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75540-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:20:53Z","timestamp":1729120853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75540-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"ISBN":["9783031755392","9783031755408"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75540-8_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"17 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tonantzintla","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}