{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T18:47:34Z","timestamp":1781722054054,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09168-7","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T12:02:20Z","timestamp":1700049740000},"page":"1761-1772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Genetic algorithm optimized a deep learning method with attention mechanism for soil moisture prediction"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1590-0023","authenticated-orcid":false,"given":"Ahmet","family":"Kara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Engin","family":"Pekel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erdener","family":"Ozcetin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gazi Bilal","family":"Y\u0131ld\u0131z","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"9168_CR1","doi-asserted-by":"publisher","first-page":"595","DOI":"10.5194\/se-6-595-2015","volume":"6","author":"Y Yu","year":"2015","unstructured":"Yu Y, Wei W, Chen LD et al (2015) Responses of vertical soil moisture to rainfall pulses and land uses in a typical loess hilly area, China. Solid Earth 6:595\u2013608. https:\/\/doi.org\/10.5194\/se-6-595-2015","journal-title":"Solid Earth"},{"key":"9168_CR2","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.jhydrol.2015.08.053","volume":"529","author":"M Augenstein","year":"2015","unstructured":"Augenstein M, Goeppert N, Goldscheider N (2015) Characterizing soil water dynamics on steep hillslopes from long-term lysimeter data. J Hydrol Amst 529:795\u2013804. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.08.053","journal-title":"J Hydrol Amst"},{"key":"9168_CR3","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.apsoil.2010.09.003","volume":"46","author":"G Roberts","year":"2010","unstructured":"Roberts G, Penwell A, Peurou F, Sharpe A (2010) The effect of soil moisture content on nitrogen transformation using OECD test guideline 216. Appl Soil Ecol 46:478\u2013482. https:\/\/doi.org\/10.1016\/j.apsoil.2010.09.003","journal-title":"Appl Soil Ecol"},{"key":"9168_CR4","doi-asserted-by":"publisher","first-page":"5281","DOI":"10.1109\/JSTARS.2015.2417832","volume":"8","author":"A Pacheco","year":"2015","unstructured":"Pacheco A, McNairn H, Mahmoodi A et al (2015) The impact of national land cover and soils data on SMOS soil moisture retrieval over canadian agricultural landscapes. IEEE J Sel Top Appl Earth Obs Remote Sens 8:5281\u20135293. https:\/\/doi.org\/10.1109\/JSTARS.2015.2417832","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"9168_CR5","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1109\/TGRS.2018.2872131","volume":"57","author":"K Fang","year":"2019","unstructured":"Fang K, Pan M, Shen C (2019) The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Trans Geosci Remote Sens 57:2221\u20132233. https:\/\/doi.org\/10.1109\/TGRS.2018.2872131","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"9168_CR6","doi-asserted-by":"publisher","first-page":"17","DOI":"10.4081\/ija.2012.e17","volume":"7","author":"G Crescimanno","year":"2012","unstructured":"Crescimanno G, Morga F, Ventrella D (2012) Application of the swap model to predict impact of climate change on soil water balance in a Sicilian vineyard. Ital J Agron 7:17. https:\/\/doi.org\/10.4081\/ija.2012.e17","journal-title":"Ital J Agron"},{"key":"9168_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/S00521-022-07529-2\/FIGURES\/10","author":"M Zeynoddin","year":"2022","unstructured":"Zeynoddin M, Bonakdari H (2022) Structural-optimized sequential deep learning methods for surface soil moisture forecasting, case study Quebec Canada. Neural Comput Appl. https:\/\/doi.org\/10.1007\/S00521-022-07529-2\/FIGURES\/10","journal-title":"Neural Comput Appl"},{"key":"9168_CR8","doi-asserted-by":"publisher","DOI":"10.1029\/2020WR028095","author":"K Fang","year":"2020","unstructured":"Fang K, Kifer D, Lawson K, Shen C (2020) Evaluating the potential and challenges of an uncertainty quantification method for long short-term memory models for soil moisture predictions. Water Resour Res. https:\/\/doi.org\/10.1029\/2020WR028095","journal-title":"Water Resour Res"},{"key":"9168_CR9","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.atmosres.2011.02.008","volume":"101","author":"V Kl\u00fcpfel","year":"2011","unstructured":"Kl\u00fcpfel V, Kalthoff N, Gantner L, Kottmeier C (2011) Evaluation of soil moisture ensemble runs to estimate precipitation variability in convection-permitting model simulations for West Africa. Atmos Res 101:178\u2013193. https:\/\/doi.org\/10.1016\/j.atmosres.2011.02.008","journal-title":"Atmos Res"},{"key":"9168_CR10","doi-asserted-by":"publisher","first-page":"e6926","DOI":"10.7717\/peerj.6926","volume":"7","author":"X Ge","year":"2019","unstructured":"Ge X, Wang J, Ding J et al (2019) Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 7:e6926. https:\/\/doi.org\/10.7717\/peerj.6926","journal-title":"PeerJ"},{"key":"9168_CR11","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.measurement.2019.06.042","volume":"146","author":"M Malajner","year":"2019","unstructured":"Malajner M, Gleich D, Planinsic P (2019) Soil type characterization for moisture estimation using machine learning and UWB-Time of Flight measurements. Measurement 146:537\u2013543. https:\/\/doi.org\/10.1016\/j.measurement.2019.06.042","journal-title":"Measurement"},{"key":"9168_CR12","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/S00704-019-03048-8\/FIGURES\/8","volume":"139","author":"E Pekel","year":"2020","unstructured":"Pekel E (2020) Estimation of soil moisture using decision tree regression. Theor Appl Climatol 139:1111\u20131119. https:\/\/doi.org\/10.1007\/S00704-019-03048-8\/FIGURES\/8","journal-title":"Theor Appl Climatol"},{"key":"9168_CR13","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1109\/JSTARS.2020.3043628","volume":"14","author":"Y Liu","year":"2021","unstructured":"Liu Y, Qian J, Yue H (2021) Combined sentinel-1A with sentinel-2A to estimate soil moisture in farmland. IEEE J Sel Top Appl Earth Obs Remote Sens 14:1292\u20131310. https:\/\/doi.org\/10.1109\/JSTARS.2020.3043628","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"9168_CR14","doi-asserted-by":"crossref","unstructured":"Hajdu I, Yule I, Dehghan-Shear MH (2018) Modelling of near-surface soil moisture using machine learning and multi-temporal sentinel 1 images in New Zealand. In: IGARSS 2018\u20132018 IEEE International geoscience and remote sensing symposium. IEEE, pp 1422\u20131425","DOI":"10.1109\/IGARSS.2018.8518657"},{"key":"9168_CR15","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/s40333-016-0049-0","volume":"8","author":"X Song","year":"2016","unstructured":"Song X, Zhang G, Liu F et al (2016) Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J Arid Land 8:734\u2013748. https:\/\/doi.org\/10.1007\/s40333-016-0049-0","journal-title":"J Arid Land"},{"key":"9168_CR16","doi-asserted-by":"publisher","first-page":"104320","DOI":"10.1016\/j.cageo.2019.104320","volume":"133","author":"KO Achieng","year":"2019","unstructured":"Achieng KO (2019) Modelling of soil moisture retention curve using machine learning techniques: artificial and deep neural networks vs support vector regression models. Comput Geosci 133:104320","journal-title":"Comput Geosci"},{"issue":"1","key":"9168_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","volume":"56","author":"CS Lee","year":"2019","unstructured":"Lee CS, Sohn E, Park JD, Jang JD (2019) Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea. GISci Remote Sens 56(1):43\u201367. https:\/\/doi.org\/10.1080\/15481603.2018.1489943","journal-title":"GISci Remote Sens"},{"key":"9168_CR18","doi-asserted-by":"publisher","first-page":"199097","DOI":"10.1109\/ACCESS.2020.3034984","volume":"8","author":"J Yu","year":"2020","unstructured":"Yu J, Tang S, Zhangzhong L et al (2020) A deep learning approach for multi-depth soil water content prediction in summer maize growth period. IEEE Access 8:199097\u2013199110. https:\/\/doi.org\/10.1109\/ACCESS.2020.3034984","journal-title":"IEEE Access"},{"key":"9168_CR19","doi-asserted-by":"publisher","first-page":"554","DOI":"10.3390\/rs13040554","volume":"13","author":"AAM Ahmed","year":"2021","unstructured":"Ahmed AAM, Deo RC, Raj N et al (2021) Deep learning forecasts of soil moisture: convolutional neural network and gated recurrent unit models coupled with satellite-derived MODIS, observations and synoptic-scale climate index data. Remote Sens Basel 13:554. https:\/\/doi.org\/10.3390\/rs13040554","journal-title":"Remote Sens Basel"},{"key":"9168_CR20","doi-asserted-by":"publisher","first-page":"5029","DOI":"10.3390\/app11115029","volume":"11","author":"K Alibabaei","year":"2021","unstructured":"Alibabaei K, Gaspar PD, Lima TM (2021) Modeling soil water content and reference evapotranspiration from climate data using deep learning method. Appl Sci 11:5029. https:\/\/doi.org\/10.3390\/app11115029","journal-title":"Appl Sci"},{"key":"9168_CR21","doi-asserted-by":"publisher","first-page":"126698","DOI":"10.1016\/j.jhydrol.2021.126698","volume":"600","author":"Q Li","year":"2021","unstructured":"Li Q, Wang Z, Shangguan W et al (2021) Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning. J Hydrol Amst 600:126698. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126698","journal-title":"J Hydrol Amst"},{"key":"9168_CR22","doi-asserted-by":"publisher","first-page":"106649","DOI":"10.1016\/j.agwat.2020.106649","volume":"245","author":"J Yu","year":"2021","unstructured":"Yu J, Zhang X, Xu L et al (2021) A hybrid CNN-GRU model for predicting soil moisture in maize root zone. Agric Water Manag 245:106649. https:\/\/doi.org\/10.1016\/j.agwat.2020.106649","journal-title":"Agric Water Manag"},{"key":"9168_CR23","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"9168_CR24","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/J.NEUCOM.2022.06.080","volume":"502","author":"Y Lei","year":"2022","unstructured":"Lei Y, Karimi HR, Chen X (2022) A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application. Neurocomputing 502:177\u2013185. https:\/\/doi.org\/10.1016\/J.NEUCOM.2022.06.080","journal-title":"Neurocomputing"},{"key":"9168_CR25","doi-asserted-by":"publisher","first-page":"104600","DOI":"10.1016\/j.envsoft.2019.104600","volume":"124","author":"B Zhang","year":"2020","unstructured":"Zhang B, Zhang H, Zhao G, Lian J (2020) Constructing a PM25 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environ Modell Softw 124:104600. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104600","journal-title":"Environ Modell Softw"},{"key":"9168_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/S11063-022-10997-1\/FIGURES\/31","author":"\u00c7 Sanatel","year":"2022","unstructured":"Sanatel \u00c7, G\u00fcnel G\u00d6 (2022) Long short term memory based self tuning regulator design for nonlinear systems. Neural Process Lett. https:\/\/doi.org\/10.1007\/S11063-022-10997-1\/FIGURES\/31","journal-title":"Neural Process Lett"},{"key":"9168_CR27","doi-asserted-by":"publisher","first-page":"10533","DOI":"10.1007\/S00521-021-06773-2\/TABLES\/6","volume":"34","author":"JF Torres","year":"2022","unstructured":"Torres JF, Mart\u00ednez-\u00c1lvarez F, Troncoso A (2022) A deep LSTM network for the Spanish electricity consumption forecasting. Neural Comput Appl 34:10533\u201310545. https:\/\/doi.org\/10.1007\/S00521-021-06773-2\/TABLES\/6","journal-title":"Neural Comput Appl"},{"key":"9168_CR28","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1007\/S13042-021-01288-7\/TABLES\/6","volume":"12","author":"F Qian","year":"2021","unstructured":"Qian F, Huang Y, Li J et al (2021) DLSA: dual-learning based on self-attention for rating prediction. Int J Mach Learn Cybern 12:1993\u20132005. https:\/\/doi.org\/10.1007\/S13042-021-01288-7\/TABLES\/6","journal-title":"Int J Mach Learn Cybern"},{"key":"9168_CR29","doi-asserted-by":"publisher","first-page":"5015","DOI":"10.1007\/S10489-021-02631-9\/FIGURES\/11","volume":"52","author":"M Yuan","year":"2022","unstructured":"Yuan M, Dai Q (2022) A novel deep pixel restoration video prediction algorithm integrating attention mechanism. Appl Intell 52:5015\u20135033. https:\/\/doi.org\/10.1007\/S10489-021-02631-9\/FIGURES\/11","journal-title":"Appl Intell"},{"key":"9168_CR30","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/J.NEUCOM.2022.06.014","volume":"501","author":"E Fu","year":"2022","unstructured":"Fu E, Zhang Y, Yang F, Wang S (2022) Temporal self-attention-based Conv-LSTM network for multivariate time series prediction. Neurocomputing 501:162\u2013173. https:\/\/doi.org\/10.1016\/J.NEUCOM.2022.06.014","journal-title":"Neurocomputing"},{"key":"9168_CR31","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1007\/S00521-022-06887-1\/TABLES\/7","volume":"34","author":"M \u0130nce","year":"2022","unstructured":"\u0130nce M (2022) Automatic and intelligent content visualization system based on deep learning and genetic algorithm. Neural Comput Appl 34:2473\u20132493. https:\/\/doi.org\/10.1007\/S00521-022-06887-1\/TABLES\/7","journal-title":"Neural Comput Appl"},{"key":"9168_CR32","unstructured":"(2022) Environmental Datasets. In: Department of geography, Geomatics and environment. https:\/\/www.utm.utoronto.ca\/geography\/resources\/meteorological-station\/environmental-datasets. Accessed 4 Jul 2022"},{"key":"9168_CR33","doi-asserted-by":"publisher","first-page":"e6988","DOI":"10.1002\/cpe.6988","volume":"34","author":"A Kara","year":"2022","unstructured":"Kara A (2022) A deep learning framework with convolutional long short-term memory for influenza-like illness trend estimation. Concurr Comput 34:e6988. https:\/\/doi.org\/10.1002\/cpe.6988","journal-title":"Concurr Comput"},{"key":"9168_CR34","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/S10115-018-1263-1\/FIGURES\/16","volume":"60","author":"MM Gowthul Alam","year":"2019","unstructured":"Gowthul Alam MM, Baulkani S (2019) Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowl Inf Syst 60:971\u20131000. https:\/\/doi.org\/10.1007\/S10115-018-1263-1\/FIGURES\/16","journal-title":"Knowl Inf Syst"},{"key":"9168_CR35","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/J.EJOR.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270:654\u2013669. https:\/\/doi.org\/10.1016\/J.EJOR.2017.11.054","journal-title":"Eur J Oper Res"},{"key":"9168_CR36","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1080\/10916466.2021.2003386","volume":"40","author":"NM Shahani","year":"2022","unstructured":"Shahani NM, Kamran M, Zheng X, Liu C (2022) Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA. Pet Sci Technol 40:534\u2013555. https:\/\/doi.org\/10.1080\/10916466.2021.2003386","journal-title":"Pet Sci Technol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09168-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09168-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09168-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T18:16:30Z","timestamp":1704996990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09168-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9168"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09168-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"24 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}