{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:40:40Z","timestamp":1765546840849,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880804"},{"type":"electronic","value":"9783030880811"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88081-1_42","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:07:08Z","timestamp":1632913628000},"page":"560-571","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Improved Forecasting Model from Satellite Imagery Based on Optimum Wavelet Bases and Adam Optimized LSTM Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7470-1788","authenticated-orcid":false,"given":"Manel","family":"Rhif","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5714-7562","authenticated-orcid":false,"given":"Ali Ben","family":"Abbes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6354-9899","authenticated-orcid":false,"given":"Beatriz","family":"Martinez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-5659","authenticated-orcid":false,"given":"Imed Riadh","family":"Farah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"42_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-030-63007-2_31","volume-title":"Computational Collective Intelligence","author":"DH Phan","year":"2020","unstructured":"Phan, D.H., Huynh, L.D.: Evaluation of the cleft-overstep algorithm for linear regression analysis. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawi\u0144ski, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 400\u2013411. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63007-2_31"},{"issue":"22","key":"42_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-06140-w","volume":"13","author":"M Rhif","year":"2020","unstructured":"Rhif, M., Ben Abbes, A., Martinez, B., Farah, I.R.: A deep learning approach for forecasting non-stationary big remote sensing time series. Arab. J. Geosci. 13(22), 1\u201311 (2020). https:\/\/doi.org\/10.1007\/s12517-020-06140-w","journal-title":"Arab. J. Geosci."},{"key":"42_CR3","doi-asserted-by":"publisher","unstructured":"Huang, N., Shen, Z., Steven, L., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454(1971), 903\u2013995 (1998). https:\/\/doi.org\/10.1098\/rspa.1998.0193","DOI":"10.1098\/rspa.1998.0193"},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Chang, Z., Zhang, Y., Chen, W.: Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187, 115804 (2019)","DOI":"10.1016\/j.energy.2019.07.134"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Li, F., Long, F., Ling, Q.: Vehicle emission forecasting based on wavelet transform and long short-term memory network. IEEE Access 6, 56984\u201356994 (2018)","DOI":"10.1109\/ACCESS.2018.2874068"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Li, Z., Tam, V.: Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20138. IEEE (2017)","DOI":"10.1109\/SSCI.2017.8280883"},{"issue":"7","key":"42_CR7","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.3390\/app9071345","volume":"9","author":"M Rhif","year":"2019","unstructured":"Rhif, M., Abbes, A.B., Farah, I., Martinez, B., Sang, Y.: Wavelet transform application for\/in non-stationary time-series analysis: a review. Appl. Sci. 9(7), 1345 (2019). https:\/\/doi.org\/10.3390\/app9071345","journal-title":"Appl. Sci."},{"key":"42_CR8","unstructured":"Rouse Jr, J., Haas, R., Schell, J., Deering, D.: Monitoring vegetation systems in the great plains with ERTS. In: Third Earth Resources Technology Satellite\u20131 Syposium 1, pp. 309\u2013317 (1974)"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Xue, J., Su, B.: Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017 (2017)","DOI":"10.1155\/2017\/1353691"},{"issue":"2","key":"42_CR10","first-page":"163","volume":"11","author":"AB Abbes","year":"2019","unstructured":"Abbes, A.B., Farah, M., Farah, I., Barra, V.: A non-stationary NDVI time series modelling using triplet Markov chain. Int. J. Inf. Dec. Sci. 11(2), 163\u2013179 (2019)","journal-title":"Int. J. Inf. Dec. Sci."},{"key":"42_CR11","doi-asserted-by":"publisher","unstructured":"Mart\u00ednez, B., Gilabert, M.: Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 113(9), 1823\u20131842 (2009). https:\/\/doi.org\/10.1016\/j.rse.2009.04.016","DOI":"10.1016\/j.rse.2009.04.016"},{"issue":"1","key":"42_CR12","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1080\/22797254.2018.1465360","volume":"51","author":"AB Abbes","year":"2018","unstructured":"Abbes, A.B., Bounouh, O., Farah, I., de Jong, R., Mart\u00ednez, B.: Comparative study of three satellite image time-series decomposition methods for vegetation change detection. Eur. J. Remote Sens. 51(1), 607\u2013615 (2018). https:\/\/doi.org\/10.1080\/22797254.2018.1465360","journal-title":"Eur. J. Remote Sens."},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Rhif, M., Ben Abbes, A., Mart\u00ednez, B., Farah, I.: Deep learning models performance for NDVI time series prediction: a case study on north west Tunisia. In: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), pp. 9\u201312. IEEE (2020)","DOI":"10.1109\/M2GARSS47143.2020.9105149"},{"issue":"6","key":"42_CR14","doi-asserted-by":"publisher","first-page":"2374","DOI":"10.1080\/01431161.2019.1688419","volume":"41","author":"E Ghaderpour","year":"2020","unstructured":"Ghaderpour, E., Ben Abbes, A., Rhif, M., Pagiatakis, S.D., Farah, I.R.: Non-stationary and unequally spaced NDVI time series analyses by the lSWAVE software. Int. J. Remote Sens. 41(6), 2374\u20132390 (2020)","journal-title":"Int. J. Remote Sens."},{"key":"42_CR15","unstructured":"Rodrigues, A., Daazmello, G., et al.: Selection of mother wavelet for wavelet analysis of vibration signals in machining. J. Mech. Eng. Autom. 6(5A), 81\u201385 (2016)"},{"issue":"1","key":"42_CR16","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s40808-018-0431-3","volume":"4","author":"D Reddy","year":"2018","unstructured":"Reddy, D., Prasad, P.: Prediction of vegetation dynamics using NDVI time series data and LSTM. Model. Earth Syst. Environ. 4(1), 409\u2013419 (2018)","journal-title":"Model. Earth Syst. Environ."},{"key":"42_CR17","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"6","key":"42_CR18","doi-asserted-by":"publisher","first-page":"6961","DOI":"10.1109\/TSG.2018.2807845","volume":"9","author":"M Rafiei","year":"2018","unstructured":"Rafiei, M., Niknam, T., Aghaei, J., Shafie-Khah, M., Catal\u00e3o, J.P.: Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine. IEEE Trans. Smart Grid 9(6), 6961\u20136971 (2018)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"7","key":"42_CR19","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"S Mallat","year":"1989","unstructured":"Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674\u2013693 (1989). https:\/\/doi.org\/10.1109\/34.192463","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Jawerth, B., Sweldens, W.: An overview of wavelet based multiresolution analyses. SIAM Rev. 36(3), 377\u2013412 (1994)","DOI":"10.1137\/1036095"},{"issue":"1","key":"42_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1556\/ComEc.5.2004.1.3","volume":"5","author":"DB Percival","year":"2004","unstructured":"Percival, D.B., Wang, M., Overland, J.E.: An introduction to wavelet analysis with applications to vegetation time series. Community Ecol. 5(1), 19\u201330 (2004). https:\/\/doi.org\/10.1556\/ComEc.5.2004.1.3","journal-title":"Community Ecol."},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Su, W., Qu, Y., Deng, C., Wang, Y., Zheng, F., Chen, Z.: Enhance generative adversarial networks by wavelet transform to denoise low-dose CT images. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 350\u2013354. IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9190766"},{"issue":"8","key":"42_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.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88081-1_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:25:09Z","timestamp":1632914709000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88081-1_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880804","9783030880811"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88081-1_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","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":"231","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":"58","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":"25% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}