{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T06:47:04Z","timestamp":1745477224520,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819915484"},{"type":"electronic","value":"9789819915491"}],"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\/978-981-99-1549-1_26","type":"book-chapter","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T09:14:00Z","timestamp":1681722840000},"page":"329-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Soil Moisture Prediction Based on LSTM-Transformer Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8195-8103","authenticated-orcid":false,"given":"Tao","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Yuanxin","family":"He","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Shengchen","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"issue":"12","key":"26_CR1","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.5194\/hess-26-3263-2022","volume":"26","author":"R Souissi","year":"2022","unstructured":"Souissi, R., et al.: Integrating process-related information into an artificial neural network for root-zone soil moisture prediction. Hydrol. Earth Syst. Sci. 26(12), 3263\u20133297 (2022)","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"2022","key":"26_CR2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.biosystemseng.2021.11.019","volume":"213","author":"N Filipovi\u0107","year":"2022","unstructured":"Filipovi\u0107, N., Brdar, S., Mimi\u0107, G., Marko, O., Crnojevi\u0107, V.: Regional soil moisture prediction system based on long short-term memory network. Biosys. Eng. 213(2022), 30\u201338 (2022)","journal-title":"Biosys. Eng."},{"issue":"1","key":"26_CR3","doi-asserted-by":"publisher","first-page":"93","DOI":"10.32604\/iasc.2021.010131","volume":"28","author":"H Niu","year":"2021","unstructured":"Niu, H., Meng, F., Yue, H., Yang, L., Dong, J., Zhang, X.: Soil moisture prediction in peri-urban Beijing, China: gene expression programming algorithm. Intell. Autom. Soft Comput. 28(1), 93\u2013106 (2021)","journal-title":"Intell. Autom. Soft Comput."},{"issue":"1","key":"26_CR4","doi-asserted-by":"publisher","first-page":"113","DOI":"10.21273\/HORTSCI.41.1.113","volume":"41","author":"G Bodo","year":"2006","unstructured":"Bodo, G.: Plant mass and yield of broccoli as affected by soil moisture. HortScience 41(1), 113\u2013118 (2006)","journal-title":"HortScience"},{"unstructured":"Beautiful grassland, the wealth of mankind. Green China 3(12) (2022)","key":"26_CR5"},{"key":"26_CR6","first-page":"20","volume":"13","author":"G Geng","year":"2022","unstructured":"Geng, G.: Protect beautiful grassland in accordence with the law. Green China 13, 20\u201323 (2022)","journal-title":"Green China"},{"issue":"18","key":"26_CR7","first-page":"159","volume":"45","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Liu, H., Wan, X., Cui, J., Zhang, F., Cai, T.: Research on soil moisture and temperature prediction based on environmental temperature and humidity. Modern Electron. Technol. 45(18), 159\u2013165 (2022)","journal-title":"Modern Electron. Technol."},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"113227","DOI":"10.1016\/j.sna.2021.113227","volume":"333","author":"YJ Chan","year":"2022","unstructured":"Chan, Y.J., Carr, A.R., Roy, S., Washburn, C.M., Neihart, N.M., Reuel, N.F.: Positionally-independent and extended read range resonant sensors applied to deep soil moisture monitoring. Sens. Actuators: A. Phys. 333, 113227 (2022)","journal-title":"Sens. Actuators: A. Phys."},{"issue":"17","key":"26_CR9","first-page":"6911","volume":"22","author":"L Yuan","year":"2022","unstructured":"Yuan, L., Fang, X., Guo, X., Ynag, L., Zhang, X., Ren, L.: Calculation of root zone soil moisture using MIV-BP neural networks. Sci. Technol. Eng. 22(17), 6911\u20136919 (2022)","journal-title":"Sci. Technol. Eng."},{"issue":"07","key":"26_CR10","first-page":"11","volume":"35","author":"X Xu","year":"2013","unstructured":"Xu, X., Yi, S., Huang, C.: Soil moisture content prediction situation review. J. Agric. Mechanization Res. 35(07), 11\u201315 (2013)","journal-title":"J. Agric. Mechanization Res."},{"issue":"09","key":"26_CR11","first-page":"1032","volume":"41","author":"G Zhang","year":"2010","unstructured":"Zhang, G., Fei, Y., Wang, H., Lian, Y.: Specific characteristics of soil hydrodynamic field state and its application to irrigational infiltration. J. Hydraul. Eng. 41(09), 1032\u20131037 (2010)","journal-title":"J. Hydraul. Eng."},{"key":"26_CR12","first-page":"121","volume":"01","author":"H Liu","year":"2004","unstructured":"Liu, H., Wu, W., Wei, C., Xie, D.: Soil water dynamics simulation by autoregression models. Mt. Res. 01, 121\u2013125 (2004)","journal-title":"Mt. Res."},{"issue":"06","key":"26_CR13","first-page":"82","volume":"28","author":"J Deng","year":"2008","unstructured":"Deng, J., Chen, X., Fang, K., Du, Z.: Prediction of chaotic soil moisture time series based on artificial neural network. Bull. Soil Water Conserv. 28(06), 82\u201385 (2008)","journal-title":"Bull. Soil Water Conserv."},{"issue":"03","key":"26_CR14","first-page":"319","volume":"43","author":"P Wang","year":"2007","unstructured":"Wang, P., Sun, W.: Comparison study on NDVI and LST based drought monitoring approaches. J. Beijing Normal Univ. (Nat. Sci.) 43(03), 319\u2013323 (2007)","journal-title":"J. Beijing Normal Univ. (Nat. Sci.)"},{"issue":"1","key":"26_CR15","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.rse.2004.05.003","volume":"92","author":"K Lee","year":"2004","unstructured":"Lee, K., Anagnostou, E.N.: A combined passive\/active microwave remote sensing approach for surfacevariable retrieval using Tropical Rainfall Measuring Mission observations. Remote Sens. Environ. 92(1), 112\u2013125 (2004)","journal-title":"Remote Sens. Environ."},{"issue":"4","key":"26_CR16","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.rse.2003.10.021","volume":"92","author":"TJ Jackson","year":"2004","unstructured":"Jackson, T.J., Chen, D.: Vegetation water content mapping using Land sat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 92(4), 225\u2013236 (2004)","journal-title":"Remote Sens. Environ."},{"issue":"02","key":"26_CR17","first-page":"222","volume":"21","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Wang, J., Bao, Y.: Soil moisture retrieval from multi-resource remotely sensed images over a wheat area. Adv. Water Sci. 21(02), 222\u2013228 (2010)","journal-title":"Adv. Water Sci."},{"unstructured":"Shikha, P., Animes, S., Sitanshu, S.: Soil moisture prediction using machine learning. In: 2018 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1\u20136. IEEE (2018)","key":"26_CR18"},{"unstructured":"Gursimran, S., Deepak, S., Amarendra, G., Sugandha, S., Shukla, A., Satish, K.: Machine learning based soil moisture prediction for internet of things based smart irrigation system. In: 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), pp. 175\u2013180. IEEE (2019)","key":"26_CR19"},{"doi-asserted-by":"crossref","unstructured":"Yu, C., Zheng, W., Zhang, X., Zhang, Z., Xue, X.: Research on soil moisture prediction model based on deep learning 14(4), (2019)","key":"26_CR20","DOI":"10.1371\/journal.pone.0214508"},{"unstructured":"Meng, C.: Research on Field Irrigation Method Based on Soil Moisture Prediction. Jilin Agricultural University (2021)","key":"26_CR21"},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.scitotenv.2022.155066","volume":"833","author":"T Nguyen","year":"2022","unstructured":"Nguyen, T., et al.: A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm. Sci. Total Environ. 833, 155 (2022)","journal-title":"Sci. Total Environ."},{"key":"26_CR23","doi-asserted-by":"publisher","first-page":"106816","DOI":"10.1016\/j.compag.2022.106816","volume":"195","author":"Q Li","year":"2022","unstructured":"Li, Q., Li, Z., Wei, S., Wan, X., Li, L., Yu, F.: Improving soil moisture prediction using a novel encoder-decoder model with residual learning. Comput. Electron. Agric. 195, 106816 (2022)","journal-title":"Comput. Electron. Agric."},{"issue":"10","key":"26_CR24","first-page":"35","volume":"39","author":"J Ma","year":"2020","unstructured":"Ma, J., Feng, K., Li, W., Hao, L., Li, Y., Gao, H.: Using water surface evaporation to estimate soil surface evaporation in arid regions in central ningxia. J. Irrig. Drainage 39(10), 35\u201341 (2020)","journal-title":"J. Irrig. Drainage"},{"issue":"02","key":"26_CR25","first-page":"18","volume":"25","author":"W Bai","year":"2009","unstructured":"Bai, W., et al.: Effect of super absorbent polymer on vertical infiltration characteristics of soil water. Trans. Chin. Soc. Agric. Eng. 25(02), 18\u201323 (2009)","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"issue":"4","key":"26_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2012.0038","volume":"12","author":"O Dani","year":"2013","unstructured":"Dani, O., Lehmann, P., Shahraeeni, E.: Advances in soil evaporation physics: a review. Vadose Zone J. 12(4), 1\u201316 (2013)","journal-title":"Vadose Zone J."},{"issue":"01","key":"26_CR27","first-page":"148","volume":"36","author":"P Liu","year":"2020","unstructured":"Liu, P., Xia, Y., Shang, M.: Estimation methods of phreatic evaporation for different textures in bare soil area. Trans. Chin. Soc. Agric. Eng. 36(01), 148\u2013153 (2020)","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"issue":"14","key":"26_CR28","doi-asserted-by":"publisher","first-page":"7647","DOI":"10.1029\/2019JD030542","volume":"124","author":"Y Jiang","year":"2019","unstructured":"Jiang, Y., Tang, R., Jiang, X., Li, Z., Gao, C.: Estimation of soil evapotranspiration and vegetation evapotranspiration using two trapezoidal models based on MODIS data. Geophys. Res. Atmos. 124(14), 7647\u20137664 (2019)","journal-title":"Geophys. Res. Atmos."},{"issue":"7","key":"26_CR29","first-page":"91","volume":"27","author":"G Zhang","year":"2013","unstructured":"Zhang, G., Wang, X., Guo, M.: The spatial and temporal structure of runoff variation and the climate background in the Yellow River basin during the past 60 years. J. Arid Land Res. Environ. 27(7), 91\u201395 (2013)","journal-title":"J. Arid Land Res. Environ."},{"issue":"3","key":"26_CR30","first-page":"27","volume":"2017","author":"K Li","year":"2017","unstructured":"Li, K., Yao, W., Xiao, P.: Advances in research on the effects of vegetation on soil infiltration and surface runoff processes. Soil Water Conserv. China 2017(3), 27\u201330 (2017)","journal-title":"Soil Water Conserv. China"},{"issue":"2","key":"26_CR31","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s10772-021-09814-2","volume":"24","author":"V Kadyan","year":"2021","unstructured":"Kadyan, V., Dua, M., Dhiman, P.: Enhancing accuracy of long contextual dependencies for Punjabi speech recognition system using deep LSTM. Int. J. Speech Technol. 24(2), 517\u2013527 (2021). https:\/\/doi.org\/10.1007\/s10772-021-09814-2","journal-title":"Int. J. Speech Technol."},{"issue":"6","key":"26_CR32","doi-asserted-by":"publisher","first-page":"102362","DOI":"10.1016\/j.ipm.2020.102362","volume":"57","author":"K Jeena","year":"2020","unstructured":"Jeena, K., Abdul, N.: An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies. Inf. Process. Manag. 57(6), 102362 (2020)","journal-title":"Inf. Process. Manag."},{"issue":"4","key":"26_CR33","doi-asserted-by":"publisher","first-page":"235","DOI":"10.2478\/jaiscr-2019-0006","volume":"9","author":"S Apeksha","year":"2019","unstructured":"Apeksha, S., Deepika, N., Simone, A.: Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J. Artif. Intell. Soft Comput. Res. 9(4), 235\u2013245 (2019)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"19","key":"26_CR34","first-page":"127","volume":"45","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Zhou, J., Ma, G., Zeng, T.: Kashi district mumps prediction model based on LSTM neural network. Mod. Electron. Tech. 45(19), 127\u2013132 (2022)","journal-title":"Mod. Electron. Tech."},{"issue":"09","key":"26_CR35","first-page":"2614","volume":"43","author":"H Guo","year":"2022","unstructured":"Guo, H., Feng, X.: CSl gesture recognition algorithm based on Bi-LSTM. Comput. Eng. Des. 43(09), 2614\u20132621 (2022)","journal-title":"Comput. Eng. Des."}],"container-title":["Communications in Computer and Information Science","Bio-Inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1549-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T19:54:59Z","timestamp":1702238099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1549-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819915484","9789819915491"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1549-1_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.bicta.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"148","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}