FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288713040> ?p ?o ?g. }

• W4288713040 abstract "Weather and Climatological studies are very important in assessing atmospheric conditions like storms and cyclones. Integrated water vapor (IWV) is an important greenhouse gas in the atmosphere responsible for the Earth's radiative balance. Global Positioning System (GPS) observations have been used for monitoring the IWV variability. The IWV estimations are carried out using ground-based GPS observations at Hyderabad (17.4°N, 78.46°E), India using GAMIT software. GAMIT is GPS analysis software developed by MIT, USA. It takes input as GPS observation data containing pseudo ranges, navigation data containing ephemeris, clock errors, g-files with orbital information, and meteorological data like pressure, temperature, and relative humidity to calculate IWV. However, estimating IWV for forecasting applications is impossible with a GPS system. This paper introduces a methodology to predict IWV during normal days and severe cyclonic events using machine learning (ML) techniques. Rational quadratic Gaussian process regression (RQ-GPR) and neural network (NN) algorithms are considered for identifying suitable ML prediction algorithms over tropical conditions. Meteorological surface data like Pressure, Temperature, and relative humidity are given as input to the machine learning models. The IWV values computed from GPS are compared with the model's predicted values. RQ-GPR model is showing good accuracy with the IWV values computed from GPS against the NN model. The correlation coefficient (ρ) achieved for RQ-GPR is 0.93, and 0.86 is obtained for the NN model.&#x0D; The RMSE (Root Mean Square Error) of the predicted IWV value with RQ-GPR is better than the NN model. We have obtained mean square error (MSE) and mean absolute error (MAE) as 18.146 kg/m2 and 3.0762 kg/m2 for RQ-GPR and 27.509 kg/m2 and 3.9102 kg/m2 for the NN model which is showing RQ-GPR is a suitable model for forecasting applications. The HUDHUD cyclonic event that occurred in October 2014 is considered for testing the proposed ML algorithms. RQ-GPR model has better results in the Prediction of IWV than the NN model. The RMSE value obtained is 2.837 kg/m2 for RQ-GPR and 3.327 kg/m2 obtained from the NN model. The results indicate that the RQ-GPR model has more accuracy than the other IWV prediction models. The prediction results are helpful for meteorology, weather, and climatology studies and useful to improve the accuracy of the regional numerical weather prediction models." @default.
• W4288713040 created "2022-07-30" @default.
• W4288713040 creator A5006912906 @default.
• W4288713040 creator A5036184176 @default.
• W4288713040 creator A5043712056 @default.
• W4288713040 creator A5050197898 @default.
• W4288713040 date "2022-07-29" @default.
• W4288713040 modified "2023-10-03" @default.
• W4288713040 title "Assessment of machine learning techniques for prediction of integrated water vapor using meteorological data" @default.
• W4288713040 doi "https://doi.org/10.15625/2615-9783/17373" @default.
• W4288713040 hasPublicationYear "2022" @default.
• W4288713040 type Work @default.
• W4288713040 citedByCount "1" @default.
• W4288713040 countsByYear W42887130402023 @default.
• W4288713040 crossrefType "journal-article" @default.
• W4288713040 hasAuthorship W4288713040A5006912906 @default.
• W4288713040 hasAuthorship W4288713040A5036184176 @default.
• W4288713040 hasAuthorship W4288713040A5043712056 @default.
• W4288713040 hasAuthorship W4288713040A5050197898 @default.
• W4288713040 hasBestOaLocation W42887130401 @default.
• W4288713040 hasConcept C105795698 @default.
• W4288713040 hasConcept C11413529 @default.
• W4288713040 hasConcept C127313418 @default.
• W4288713040 hasConcept C127413603 @default.
• W4288713040 hasConcept C139945424 @default.
• W4288713040 hasConcept C146978453 @default.
• W4288713040 hasConcept C147534773 @default.
• W4288713040 hasConcept C147947694 @default.
• W4288713040 hasConcept C153294291 @default.
• W4288713040 hasConcept C19269812 @default.
• W4288713040 hasConcept C205649164 @default.
• W4288713040 hasConcept C33923547 @default.
• W4288713040 hasConcept C39432304 @default.
• W4288713040 hasConcept C41008148 @default.
• W4288713040 hasConcept C60229501 @default.
• W4288713040 hasConcept C62649853 @default.
• W4288713040 hasConcept C64828349 @default.
• W4288713040 hasConcept C76155785 @default.
• W4288713040 hasConceptScore W4288713040C105795698 @default.
• W4288713040 hasConceptScore W4288713040C11413529 @default.
• W4288713040 hasConceptScore W4288713040C127313418 @default.
• W4288713040 hasConceptScore W4288713040C127413603 @default.
• W4288713040 hasConceptScore W4288713040C139945424 @default.
• W4288713040 hasConceptScore W4288713040C146978453 @default.
• W4288713040 hasConceptScore W4288713040C147534773 @default.
• W4288713040 hasConceptScore W4288713040C147947694 @default.
• W4288713040 hasConceptScore W4288713040C153294291 @default.
• W4288713040 hasConceptScore W4288713040C19269812 @default.
• W4288713040 hasConceptScore W4288713040C205649164 @default.
• W4288713040 hasConceptScore W4288713040C33923547 @default.
• W4288713040 hasConceptScore W4288713040C39432304 @default.
• W4288713040 hasConceptScore W4288713040C41008148 @default.
• W4288713040 hasConceptScore W4288713040C60229501 @default.
• W4288713040 hasConceptScore W4288713040C62649853 @default.
• W4288713040 hasConceptScore W4288713040C64828349 @default.
• W4288713040 hasConceptScore W4288713040C76155785 @default.
• W4288713040 hasLocation W42887130401 @default.
• W4288713040 hasOpenAccess W4288713040 @default.
• W4288713040 hasPrimaryLocation W42887130401 @default.
• W4288713040 hasRelatedWork W1965087930 @default.
• W4288713040 hasRelatedWork W1973194845 @default.
• W4288713040 hasRelatedWork W2023674185 @default.
• W4288713040 hasRelatedWork W2127642947 @default.
• W4288713040 hasRelatedWork W2325081723 @default.
• W4288713040 hasRelatedWork W2383506387 @default.
• W4288713040 hasRelatedWork W2507810274 @default.
• W4288713040 hasRelatedWork W282695260 @default.
• W4288713040 hasRelatedWork W4383907499 @default.
• W4288713040 hasRelatedWork W795023631 @default.
• W4288713040 isParatext "false" @default.
• W4288713040 isRetracted "false" @default.
• W4288713040 workType "article" @default.