{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:42:43Z","timestamp":1742967763775,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970216"},{"type":"electronic","value":"9789819970223"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-7022-3_1","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Spatial Interpolation Method Based on BP Neural Network with Bellman Equation"],"prefix":"10.1007","author":[{"given":"Liang","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"234","DOI":"10.2307\/143141","volume":"46","author":"WR Tobler","year":"1970","unstructured":"Tobler, W.R.: A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46, 234\u2013240 (1970)","journal-title":"Econ. Geogr."},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Nurhadiyatna, A., Sunaryani, A., Sudriani, Y., Latifah, A.: 2D spatial interpolation for water quality parameter distribution in Maninjau Lake. In: 2016 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 215\u2013220. IEEE (2016)","DOI":"10.1109\/IC3INA.2016.7863052"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ecolind.2014.04.003","volume":"45","author":"F Dai","year":"2014","unstructured":"Dai, F., Zhou, Q., Lv, Z., Wang, X., Liu, G.: Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol. Ind. 45, 184\u2013194 (2014)","journal-title":"Ecol. Ind."},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3390\/ijgi6090283","volume":"6","author":"P Tziachris","year":"2017","unstructured":"Tziachris, P., Metaxa, E., Papadopoulos, F., Papadopoulou, M.: Spatial modelling and prediction assessment of soil iron using kriging interpolation with pH as auxiliary information. ISPRS Int. J. Geo Inf. 6, 283 (2017)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Viana, D., Barbosa, L.: Attention-based spatial interpolation for house price prediction. In: Proceedings of the 29th International Conference on Advances in Geographic Information Systems, pp. 540\u2013549 (2021)","DOI":"10.1145\/3474717.3484257"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"5626","DOI":"10.3390\/rs14215626","volume":"14","author":"Y Tang","year":"2022","unstructured":"Tang, Y., et al.: Spatial estimation of regional PM2.5 concentrations with GWR models using PCA and RBF interpolation optimization. Remote Sens. 14, 5626 (2022)","journal-title":"Remote Sens."},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"133857","DOI":"10.1016\/j.jclepro.2022.133857","volume":"374","author":"F Soto","year":"2022","unstructured":"Soto, F., Navarro, F., D\u00edaz, G., Emery, X., Parviainen, A., Ega\u00f1a, \u00c1.: Transitive kriging for modeling tailings deposits: a case study in southwest Finland. J. Clean. Prod. 374, 133857 (2022)","journal-title":"J. Clean. Prod."},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Le, N.D., Zidek, J.V.: Statistical analysis of environmental space-time processes. Springer, New York (2006). https:\/\/doi.org\/10.1007\/0-387-35429-8","DOI":"10.1007\/0-387-35429-8"},{"key":"1_CR9","unstructured":"Hecht-Nielsen, R.: Kolmogorov\u2019s mapping neural network existence theorem. In: Proceedings of the International Conference on Neural Networks, pp. 11\u201314. IEEE Press New York, NY, USA (1987)"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"153770","DOI":"10.1016\/j.scitotenv.2022.153770","volume":"823","author":"Y Lai","year":"2022","unstructured":"Lai, Y., et al.: Reconstructing the data gap between GRACE and GRACE follow-on at the basin scale using artificial neural network. Sci. Total. Environ. 823, 153770 (2022)","journal-title":"Sci. Total. Environ."},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"104149","DOI":"10.1016\/j.catena.2019.104149","volume":"182","author":"M Shahriari","year":"2019","unstructured":"Shahriari, M., Delbari, M., Afrasiab, P., Pahlavan-Rad, M.R.: Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: a case of southeastern Iran. CATENA 182, 104149 (2019)","journal-title":"CATENA"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.envsoft.2013.12.008","volume":"53","author":"J Li","year":"2014","unstructured":"Li, J., Heap, A.D.: Spatial interpolation methods applied in the environmental sciences: a review. Environ Model Softw. 53, 173\u2013189 (2014)","journal-title":"Environ Model Softw."},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.catena.2018.11.037","volume":"174","author":"A Sergeev","year":"2019","unstructured":"Sergeev, A., Buevich, A., Baglaeva, E., Shichkin, A.J.C.: Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. CATENA 174, 425\u2013435 (2019)","journal-title":"CATENA"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Zhu, D., Cheng, X., Zhang, F., Yao, X., Gao, Y., Liu, Y.: Spatial interpolation using conditional generative adversarial neural networks. Res. Output Contrib. J. 34, 735\u2013758 (2020)","DOI":"10.1080\/13658816.2019.1599122"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Luo, P., Song, Y., Zhu, D., Cheng, J., Meng, L.: A generalized heterogeneity model for spatial interpolation. Int. J. Geograph. Inf. Sci. 37, 634\u2013659 (2023)","DOI":"10.1080\/13658816.2022.2147530"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Lee, M.-H., Chen, Y.J.: Markov chain random field kriging for estimating extreme precipitation at unevenly distributed sites. J. Hydrol. 616, 128591 (2023)","DOI":"10.1016\/j.jhydrol.2022.128591"},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.compgeo.2011.02.011","volume":"38","author":"HI Park","year":"2011","unstructured":"Park, H.I., Lee, S.R.: Evaluation of the compression index of soils using an artificial neural network. Comput. Geotech. 38, 472\u2013481 (2011)","journal-title":"Comput. Geotech."},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"8390","DOI":"10.1002\/joc.7731","volume":"42","author":"AC Xavier","year":"2022","unstructured":"Xavier, A.C., Scanlon, B.R., King, C.W., Alves, A.I.: New improved Brazilian daily weather gridded data (1961\u20132020). Int. J. Climatol. 42, 8390\u20138404 (2022)","journal-title":"Int. J. Climatol."},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"102671","DOI":"10.1016\/j.trc.2020.102671","volume":"117","author":"Z Cui","year":"2020","unstructured":"Cui, Z., Lin, L., Pu, Z., Wang, Y.: Graph Markov network for traffic forecasting with missing data. Transp. Res. Part C Emerg. Technol. 117, 102671 (2020)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1109\/TIP.2013.2290586","volume":"23","author":"F Vedadi","year":"2013","unstructured":"Vedadi, F., Shirani, S.: A map-based image interpolation method via viterbi decoding of Markov chains of interpolation functions. IEEE Trans. Image Process. 23, 424\u2013438 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"109082","DOI":"10.1016\/j.patcog.2022.109082","volume":"134","author":"M Trombini","year":"2023","unstructured":"Trombini, M., Solarna, D., Moser, G., Dellepiane, S.: A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields. Pattern Recogn. 134, 109082 (2023)","journal-title":"Pattern Recogn."},{"key":"1_CR22","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.image.2012.07.001","volume":"28","author":"S Colonnese","year":"2013","unstructured":"Colonnese, S., Rinauro, S., Scarano, G.: Bayesian image interpolation using Markov random fields driven by visually relevant image features. Sig. Process. Image Commun. 28, 967\u2013983 (2013)","journal-title":"Sig. Process. Image Commun."},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Zhu, L., Hou, G., Song, X., Wei, Y., Wang, Y.: A spatial interpolation using clustering adaptive inverse distance weighting algorithm with linear regression. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022. LNCS, Singapore, 6\u20138 August 2022, Proceedings, Part II, pp. 261\u2013272. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-10986-7_21","DOI":"10.1007\/978-3-031-10986-7_21"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Liu, F., et al.: Mapping high resolution National Soil Information Grids of China. Sci. Bull. 67(3), 328\u2013340 (2022). https:\/\/doi.org\/10.1016\/j.scib.2021.10.013","DOI":"10.1016\/j.scib.2021.10.013"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1109\/LGRS.2018.2832647","volume":"15","author":"K Ishitsuka","year":"2018","unstructured":"Ishitsuka, K., Mogi, T., Sugano, K., Yamaya, Y., Uchida, T., Kajiwara, T.: Resistivity-based temperature estimation of the Kakkonda Geothermal Field, Japan, using a neural network and neural kriging. IEEE Geosci. Remote Sens. Lett. 15, 1154\u20131158 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"1_CR26","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.scitotenv.2008.03.011","volume":"398","author":"C Zhang","year":"2008","unstructured":"Zhang, C., Luo, L., Xu, W., Ledwith, V.: Use of local Moran\u2019s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci. Total. Environ. 398, 212\u2013221 (2008)","journal-title":"Sci. Total. Environ."},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"3063","DOI":"10.1007\/s00477-022-02180-8","volume":"36","author":"R Peli","year":"2022","unstructured":"Peli, R., Menafoglio, A., Cervino, M., Dovera, L., Secchi, P.: Physics-based Residual Kriging for dynamically evolving functional random fields. Stoch. Env. Res. Risk Assess. 36, 3063\u20133080 (2022)","journal-title":"Stoch. Env. Res. Risk Assess."},{"key":"1_CR28","doi-asserted-by":"publisher","first-page":"120697","DOI":"10.1016\/j.envpol.2022.120697","volume":"316","author":"PC Agyeman","year":"2023","unstructured":"Agyeman, P.C., Kingsley, J., Kebonye, N.M., Khosravi, V., Bor\u016fvka, L., Va\u0161\u00e1t, R.: Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging. Environ. Pollut. 316, 120697 (2023)","journal-title":"Environ. Pollut."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7022-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:08:39Z","timestamp":1699574919000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7022-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970216","9789819970223"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7022-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"422","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":"95","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":"36","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":"23% - 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":"3.4","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":"3.1","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)"}}]}}