{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T05:28:39Z","timestamp":1744954119186,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784899"},{"type":"electronic","value":"9789819784905"}],"license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"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\/978-981-97-8490-5_15","type":"book-chapter","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:09:07Z","timestamp":1730884147000},"page":"203-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatio-temporal Perceiving Network Based Vision Transformer for\u00a06-Hour Precipitation Prediction Using Multi-meteorological Factors"],"prefix":"10.1007","author":[{"given":"Jing","family":"Hu","sequence":"first","affiliation":[]},{"given":"Peng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Honghu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"15_CR1","first-page":"1","volume":"19","author":"C Bai","year":"2022","unstructured":"Bai, C., Sun, F., Zhang, J., Song, Y., Chen, S.: Rainformer: features extraction balanced network for radar-based precipitation nowcasting. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"15_CR2","volume":"7","author":"AY Barrera-Animas","year":"2022","unstructured":"Barrera-Animas, A.Y., Oyedele, L.O., Bilal, M., Akinosho, T.D., Delgado, J.M.D., Akanbi, L.A.: Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting. Mach. Learn. Appl. 7, 100204 (2022)","journal-title":"Mach. Learn. Appl."},{"issue":"1","key":"15_CR3","first-page":"1525","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R., et al.: Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. 7(1), 1525\u20131534 (2014)","journal-title":"Geosci. Model Dev. Discuss."},{"key":"15_CR4","first-page":"26950","volume":"34","author":"Z Chang","year":"2021","unstructured":"Chang, Z., Zhang, X., Wang, S., Ma, S., Ye, Y., Xinguang, X., Gao, W.: Mau: a motion-aware unit for video prediction and beyond. Adv. Neural. Inf. Process. Syst. 34, 26950\u201326962 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"15_CR5","first-page":"273","volume":"38","author":"YY Cheng","year":"2023","unstructured":"Cheng, Y.Y., Chang, C.T., Chen, B.F., Kuo, H.C., Lee, C.S.: Extracting 3d radar features to improve quantitative precipitation estimation in complex terrain based on deep learning neural networks. Weather Forecast. 38(2), 273\u2013289 (2023)","journal-title":"Weather Forecast."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. Noise Reduction in Speech Processing, pp.\u00a01\u20134 (2009)","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"15_CR7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al.: An Image is Worth 16 $$\\times $$ 16 Words: Transformers for Image Recognition at Scale (2020). arXiv:2010.11929"},{"key":"15_CR8","unstructured":"Duncan, J., Subramanian, S., Harrington, P.: Generative Modeling of High-resolution Global Precipitation Forecasts (2022). arXiv:2210.12504"},{"issue":"1","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-32483-x","volume":"13","author":"L Espeholt","year":"2022","unstructured":"Espeholt, L., Agrawal, S., S\u00f8nderby, C., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R., Andrychowicz, M., Hickey, J., et al.: Deep learning for twelve hour precipitation forecasts. Nat. Commun. 13(1), 1\u201310 (2022)","journal-title":"Nat. Commun."},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.neunet.2021.08.036","volume":"144","author":"JG Fern\u00e1ndez","year":"2021","unstructured":"Fern\u00e1ndez, J.G., Mehrkanoon, S.: Broad-unet: multi-scale feature learning for nowcasting tasks. Neural Netw. 144, 419\u2013427 (2021)","journal-title":"Neural Netw."},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Gao, Z., Tan, C., Wu, L., Li, S.Z.: Simvp: simpler yet better video prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3170\u20133180 (2022)","DOI":"10.1109\/CVPR52688.2022.00317"},{"key":"15_CR12","first-page":"25390","volume":"35","author":"Z Gao","year":"2022","unstructured":"Gao, Z., Shi, X., Wang, H., Zhu, Y., Wang, Y.B., Li, M., Yeung, D.Y.: Earthformer: exploring space-time transformers for earth system forecasting. Adv. Neural. Inf. Process. Syst. 35, 25390\u201325403 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"15_CR13","first-page":"52","volume":"1","author":"RL Gorsuch","year":"2010","unstructured":"Gorsuch, R.L., Lehmann, C.S.: Correlation coefficients: mean bias and confidence interval distortions. J. Methods Meas. Soc. Sci. 1(2), 52\u201365 (2010)","journal-title":"J. Methods Meas. Soc. Sci."},{"issue":"6","key":"15_CR14","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.3390\/rs15061529","volume":"15","author":"S Guo","year":"2023","unstructured":"Guo, S., Sun, N., Pei, Y., Li, Q.: 3d-unet-LSTM: a deep learning-based radar echo extrapolation model for convective nowcasting. Remote Sens. 15(6), 1529 (2023)","journal-title":"Remote Sens."},{"issue":"730","key":"15_CR15","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u00e1nyi, A., Mu\u00f1oz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al.: The era5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730), 1999\u20132049 (2020)","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Hu, Y., Chen, L., Wang, Z., Li, H.: Swinvrnn: A data-driven ensemble forecasting model via learned distribution perturbation. J. Adv. Model. Earth Syst. 15(2), e2022MS003211 (2023)","DOI":"10.1029\/2022MS003211"},{"issue":"1","key":"15_CR17","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s00382-021-05916-4","volume":"58","author":"W Li","year":"2022","unstructured":"Li, W., Gao, X., Hao, Z., Sun, R.: Using deep learning for precipitation forecasting based on spatio-temporal information: a case study. Clim. Dyn. 58(1), 443\u2013457 (2022)","journal-title":"Clim. Dyn."},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"15_CR19","unstructured":"Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., et\u00a0al.: Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators (2022). arXiv:2202.11214"},{"key":"15_CR20","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 (2015)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Tan, C., Gao, Z., Wu, L., Xu, Y., Xia, J., Li, S., Li, S.Z.: Temporal attention unit: towards efficient spatiotemporal predictive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18770\u201318782 (2023)","DOI":"10.1109\/CVPR52729.2023.01800"},{"key":"15_CR22","unstructured":"Tan, C., Li, S., Gao, Z., Guan, W., Wang, Z., Liu, Z., Wu, L., Li, S.Z.: Openstl: a comprehensive benchmark of spatio-temporal predictive learning. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.patrec.2021.01.036","volume":"145","author":"K Trebing","year":"2021","unstructured":"Trebing, K., Stanczyk, T., Mehrkanoon, S.: Smaat-unet: precipitation nowcasting using a small attention-unet architecture. Pattern Recogn. Lett. 145, 178\u2013186 (2021)","journal-title":"Pattern Recogn. Lett."},{"key":"15_CR24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, \u0141., Polosukhin, I.: Attention is all you need in advances in neural information processing systems. Search PubMed, pp. 5998\u20136008 (2017)"},{"issue":"10","key":"15_CR25","doi-asserted-by":"publisher","first-page":"6355","DOI":"10.5194\/gmd-14-6355-2021","volume":"14","author":"J Wang","year":"2021","unstructured":"Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., Kotamarthi, V.R.: Fast and accurate learned multiresolution dynamical downscaling for precipitation. Geosci. Model Dev. 14(10), 6355\u20136372 (2021)","journal-title":"Geosci. Model Dev."},{"key":"15_CR26","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., Philip, S.Y.: Predrnn++: towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In: International Conference on Machine Learning, pp. 5123\u20135132. PMLR (2018)"},{"key":"15_CR27","unstructured":"Wang, Y., Long, M., Wang, J., Gao, Z., Yu, P.S.: Predrnn: recurrent neural networks for predictive learning using spatiotemporal LSTMS. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"2","key":"15_CR28","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1109\/TPAMI.2022.3165153","volume":"45","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Wu, H., Zhang, J., Gao, Z., Wang, J., Philip, S.Y., Long, M.: PredRNN: a recurrent neural network for spatiotemporal predictive learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2208\u20132225 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., Yu, P.S.: Memory in memory: a predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9154\u20139162 (2019)","DOI":"10.1109\/CVPR.2019.00937"},{"issue":"1","key":"15_CR30","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1109\/TBDATA.2018.2871151","volume":"6","author":"P Zhang","year":"2018","unstructured":"Zhang, P., Jia, Y., Gao, J., Song, W., Leung, H.: Short-term rainfall forecasting using multi-layer perceptron. IEEE Trans. Big Data 6(1), 93\u2013106 (2018)","journal-title":"IEEE Trans. Big Data"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Zhong, X., Chen, L., Liu, J., Lin, C., Qi, Y., Li, H.: Fuxi-extreme: Improving Extreme Rainfall and Wind Forecasts with Diffusion Model (2023). arXiv:2310.19822","DOI":"10.1007\/s11430-023-1427-x"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8490-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:13:41Z","timestamp":1730884421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8490-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"ISBN":["9789819784899","9789819784905"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8490-5_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"7 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}