Artificial Neural Network for Predicting Global Sub-Daily Tropospheric Wet Delay
These are the full results from "Artificial Neural Network for Predicting global sub-daily Tropospheric Wet Delay" for the selected 505 stations. They are divided into: 1- Raw time series of the 505 stations for (ZWD, Pressure, Temperature and Integrated Water Vapour). 2- The R values betw...
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| creator | Mohammed, Jareer |
| description | These are the full results from "Artificial Neural Network for Predicting global sub-daily Tropospheric Wet Delay" for the selected 505 stations. They are divided into: 1- Raw time series of the 505 stations for (ZWD, Pressure, Temperature and Integrated Water Vapour). 2- The R values between the actual and the predicted ZWD. 3- The Predicted and actual ZWD. 4- The difference between the actual and predicted ZWD. 5- the inputs files for the ANN processing (the time series in dat format). |
| doi_str_mv | 10.17632/hy767fh3rx.2 |
| format | Dataset |
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They are divided into: 1- Raw time series of the 505 stations for (ZWD, Pressure, Temperature and Integrated Water Vapour). 2- The R values between the actual and the predicted ZWD. 3- The Predicted and actual ZWD. 4- The difference between the actual and predicted ZWD. 5- the inputs files for the ANN processing (the time series in dat format).</description><subject>Artificial Neural Networks</subject><subject>Tropospheric Propagation Delays</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2021</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVjrsKwjAUQLM4iDq65wda-4B2FutjkoIFx3CbJu3FaMptiubvlSI4O53hnOEwto6jMM6zNNl0Ps9y3aX0CpM5K7fkUKNEMPysRprgnpZuXFviJakGpcNHy4_G1h97GeugADSeV2R7O_SdIpT8qhwvlAG_ZDMNZlCrLxcsOOyr3SlowIFEp0RPeAfyIo7EdCR-RyJJ_-3fvc5F-g</recordid><startdate>20210129</startdate><enddate>20210129</enddate><creator>Mohammed, Jareer</creator><general>Mendeley</general><scope>DYCCY</scope><scope>PQ8</scope></search><sort><creationdate>20210129</creationdate><title>Artificial Neural Network for Predicting Global Sub-Daily Tropospheric Wet Delay</title><author>Mohammed, Jareer</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_17632_hy767fh3rx_23</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Neural Networks</topic><topic>Tropospheric Propagation Delays</topic><toplevel>online_resources</toplevel><creatorcontrib>Mohammed, Jareer</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mohammed, Jareer</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Artificial Neural Network for Predicting Global Sub-Daily Tropospheric Wet Delay</title><date>2021-01-29</date><risdate>2021</risdate><abstract>These are the full results from "Artificial Neural Network for Predicting global sub-daily Tropospheric Wet Delay" for the selected 505 stations. They are divided into: 1- Raw time series of the 505 stations for (ZWD, Pressure, Temperature and Integrated Water Vapour). 2- The R values between the actual and the predicted ZWD. 3- The Predicted and actual ZWD. 4- The difference between the actual and predicted ZWD. 5- the inputs files for the ANN processing (the time series in dat format).</abstract><pub>Mendeley</pub><doi>10.17632/hy767fh3rx.2</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.17632/hy767fh3rx.2 |
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| language | eng |
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| subjects | Artificial Neural Networks Tropospheric Propagation Delays |
| title | Artificial Neural Network for Predicting Global Sub-Daily Tropospheric Wet Delay |
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