乐胖代购免代理版

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The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision\u2010recall curve and F\u2010measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F\u2010measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial\u2013temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation\u00a0strategies.<\/jats:p>","DOI":"10.1111\/mice.12629","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T12:15:38Z","timestamp":1602159338000},"page":"402-420","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data"],"prefix":"10.1111","volume":"36","author":[{"given":"Shangjia","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Delaware, Newark, DE USA"}]},{"given":"Tianbo","family":"Yu","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering Texas A&M University, College Station, TX USA"}]},{"given":"Hamed","family":"Farahmand","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering Texas A&M University, College Station, TX USA"}]},{"given":"Ali","family":"Mostafavi","sequence":"additional","affiliation":[{"name":"Zachry Department of Civil and Environmental Engineering Texas A&M University, College Station, TX USA"}]}],"member":"311","published-online":{"date-parts":[[2020,10,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-018-0085-1"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-2010-0345"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/ab6edd"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0198-9715(01)00010-2"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(124)"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12517"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2416723"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)WR.1943-5452.0001087"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SmartTechCon.2017.8358367"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0349-y"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2010.02.037"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-9496(2003)129:6(458)"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advwatres.2005.03.001"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12263"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.1015"},{"key":"e_1_2_9_17_1","volume-title":"Flood forecasting using machine learning methods","author":"Chang F.\u2010J.","year":"2019"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12495"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134979"},{"key":"e_1_2_9_20_1","unstructured":"Cui Z. 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