{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:04:41Z","timestamp":1783577081371,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,29]],"date-time":"2020-03-29T00:00:00Z","timestamp":1585440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41971351, 41771422, 41890822"],"award-info":[{"award-number":["41971351, 41771422, 41890822"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation\u2013water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation\u2013water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.<\/jats:p>","DOI":"10.3390\/rs12071096","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6624-6693","authenticated-orcid":false,"given":"Zeqiang","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-9972","authenticated-orcid":false,"given":"Nengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, N., Xiong, C., Du, W., Wang, C., Lin, X., and Chen, Z. (2019). An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions. Water, 11.","DOI":"10.3390\/w11091795"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1029\/94JD00483","article-title":"A simple hydrologically based model of land surface water and energy fluxes for general circulation models","volume":"99","author":"Liang","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.13031\/2013.42256","article-title":"SWAT: Model use, calibration, and validation","volume":"55","author":"Arnold","year":"2012","journal-title":"Trans. ASABE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/0022-1694(92)90096-E","article-title":"The Xinanjiang model applied in China","volume":"135","year":"1992","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jhydrol.2014.07.044","article-title":"Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau","volume":"519","author":"Tong","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_6","first-page":"237","article-title":"Capability of TMPA products to simulate streamflow in upper Yellow and Yangtze River basins on Tibetan Plateau","volume":"7","author":"Hao","year":"2014","journal-title":"Water Sci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, X., Yang, T., Hsu, K., Liu, C., and Sorooshian, S. (2017). Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau. Hydrol. Earth Syst. Sci. (Online), 21.","DOI":"10.5194\/hess-2016-282"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1145\/2500499","article-title":"Data science and prediction","volume":"56","author":"Dhar","year":"2013","journal-title":"Commun. ACM"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2084","DOI":"10.1080\/02626667.2015.1083650","article-title":"Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India","volume":"61","author":"Khan","year":"2016","journal-title":"Hydrol. Sci. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.jhydrol.2008.03.017","article-title":"Structural optimisation and input selection of an artificial neural network for river level prediction","volume":"355","author":"Leahy","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","article-title":"Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications","volume":"15","author":"Maier","year":"2000","journal-title":"Environ. Model. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3846\/16486897.2017.1303498","article-title":"Forecasting surface water level fluctuations of lake Serwy (Northeastern Poland) by artificial neural networks and multiple linear regression","volume":"25","author":"Piasecki","year":"2017","journal-title":"J. Environ. Eng. Landsc. Manag."},{"key":"ref_13","first-page":"174","article-title":"Water level forecasting using a hybrid algorithm of artificial neural networks and local Kalman filtering","volume":"233","author":"Zhong","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9323","DOI":"10.1109\/TGRS.2019.2926110","article-title":"A Data-Driven Approach for Accurate Rainfall Prediction","volume":"57","author":"Manandhar","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sahoo, A., Samantaray, S., Bankuru, S., and Ghose, D.K. (2020). Prediction of Flood Using Adaptive Neuro-Fuzzy Inference Systems: A Case Study. Smart Intelligent Computing and Applications, Springer.","DOI":"10.1007\/978-981-13-9282-5_70"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Samantaray, S., and Sahoo, A. (2020). Appraisal of runoff through BPNN, RNN, and RBFN in Tentulikhunti Watershed: A case study. Frontiers in Intelligent Computing: Theory and Applications, Springer.","DOI":"10.1007\/978-981-13-9920-6_26"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Samantaray, S., and Sahoo, A. (2020). Estimation of runoff through BPNN and SVM in Agalpur Watershed. Frontiers in Intelligent Computing: Theory and Applications, Springer.","DOI":"10.1007\/978-981-13-9920-6_27"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Garcia, F.C.C., Retamar, A.E., and Javier, J.C. (2016, January 22\u201325). Development of a predictive model for on-demand remote river level nowcasting: Case study in Cagayan River Basin, Philippines. Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore.","DOI":"10.1109\/TENCON.2016.7848657"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Adnan, R., Ruslan, F.A., Samad, A.M., and Zain, Z.M. (2012, January 16\u201317). Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor. Proceedings of the 2012 IEEE Control and System Graduate Research Colloquium, Shah Alam, Selangor, Malaysia.","DOI":"10.1109\/ICSGRC.2012.6287127"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jhydrol.2016.05.014","article-title":"A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products","volume":"539","author":"Nanda","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jhydrol.2013.05.038","article-title":"Reinforced recurrent neural networks for multi-step-ahead flood forecasts","volume":"497","author":"Chen","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.jhydrol.2012.10.054","article-title":"Using self-organizing maps and wavelet transforms for space\u2013time pre-processing of satellite precipitation and runoff data in neural network based rainfall\u2013runoff modeling","volume":"476","author":"Nourani","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_24","unstructured":"Huang, Y. (2017). Application of TRMM Precipitation in VIC Hydrological Model for Streamflow Simulations in the Upstream of Yangtze River. [Master\u2019s Thesis, Nanjing Forestry University]."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, H., Zhang, X., and Liu, G. (2019). Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model. Water, 11.","DOI":"10.3390\/w11020253"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.scitotenv.2016.08.034","article-title":"Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy)","volume":"573","author":"Tuo","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.jhydrol.2006.05.036","article-title":"Sediment and runoff changes in the Yangtze River basin during past 50 years","volume":"331","author":"Zhang","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bai, L., Shi, C., Li, L., Yang, Y., and Wu, J. (2018). Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sens., 10.","DOI":"10.3390\/rs10030362"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.1175\/JCLI3790.1","article-title":"Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling","volume":"19","author":"Sheffield","year":"2006","journal-title":"J. Clim."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1175\/JHM-D-15-0191.1","article-title":"Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale","volume":"17","author":"Wang","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.atmosres.2014.12.015","article-title":"Comparison of versions 6 and 7 3-hourly TRMM multi-satellite precipitation analysis (TMPA) research products","volume":"163","author":"Liu","year":"2015","journal-title":"Atmos. Res."},{"key":"ref_34","first-page":"44","article-title":"Modeling and analysis of effects of precipitation and vegetation coverage on runoff and sediment yield in Jinsha River Basin","volume":"6","author":"Du","year":"2013","journal-title":"Water Sci. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3481","DOI":"10.5194\/gmd-11-3481-2018","article-title":"The Variable Infiltration Capacity model version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility","volume":"11","author":"Hamman","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_37","unstructured":"James, A.A., and Edward, R. (1988). Learning representations by back-propagating errors. Neurocomputing: Foundations of Research, MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/72.548162","article-title":"Learning long-term dependencies in NARX recurrent neural networks","volume":"7","author":"Lin","year":"1996","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Thomas, A.J., Petridis, M., Walters, S.D., Gheytassi, S.M., and Morgan, R.E. (2017, January 25\u201327). Two hidden layers are usually better than one. Proceedings of the International Conference on Engineering Applications of Neural Networks, Athens, Greece.","DOI":"10.1007\/978-3-319-65172-9_24"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jaridenv.2016.12.009","article-title":"Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil","volume":"139","author":"Barbosa","year":"2017","journal-title":"J. Arid Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1096\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:12Z","timestamp":1760173992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,29]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071096"],"URL":"https:\/\/doi.org\/10.3390\/rs12071096","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,29]]}}}