Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models.
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| Title: | Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models. |
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| Authors: | Khaniani, Ali Sam1 (AUTHOR) ali.sam@nit.ac.ir, Motieyan, Hamid1 (AUTHOR) h.motieyan@nit.ac.ir, Mohammadi, Atefeh2 (AUTHOR) mohamadi.atefeh@yahoo.com |
| Source: | Journal of Atmospheric & Solar-Terrestrial Physics. Mar2021, Vol. 214, pN.PAG-N.PAG. 1p. |
| Subjects: | Artificial neural networks, Precipitable water, Water vapor, Rainfall, Standard deviations, Global modeling systems |
| Geographic Terms: | Tehran (Iran) |
| Abstract: | In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events. • The MLP and NARX networks were proposed for rainfall forecasting in Tehran. • By removing PWV values from the inputs the precision of MLP is reduced for the rainfall ranges less than 6 mm/h. • For the range of precipitation above 3 mm/h, the use of PWV has a positive impact on the output of the NARX model. • The best results were achieved from 4years trained MLP and 2years trained NARX model. • For almost all seasons, the underestimation of the NARX model was less than MLP. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Atmospheric & Solar-Terrestrial Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 148545060 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khaniani%2C+Ali+Sam%22">Khaniani, Ali Sam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ali.sam@nit.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Motieyan%2C+Hamid%22">Motieyan, Hamid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> h.motieyan@nit.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Mohammadi%2C+Atefeh%22">Mohammadi, Atefeh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mohamadi.atefeh@yahoo.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Atmospheric+%26+Solar-Terrestrial+Physics%22">Journal of Atmospheric & Solar-Terrestrial Physics</searchLink>. Mar2021, Vol. 214, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitable+water%22">Precipitable water</searchLink><br /><searchLink fieldCode="DE" term="%22Water+vapor%22">Water vapor</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Global+modeling+systems%22">Global modeling systems</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Tehran+%28Iran%29%22">Tehran (Iran)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events. • The MLP and NARX networks were proposed for rainfall forecasting in Tehran. • By removing PWV values from the inputs the precision of MLP is reduced for the rainfall ranges less than 6 mm/h. • For the range of precipitation above 3 mm/h, the use of PWV has a positive impact on the output of the NARX model. • The best results were achieved from 4years trained MLP and 2years trained NARX model. • For almost all seasons, the underestimation of the NARX model was less than MLP. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Atmospheric & Solar-Terrestrial Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jastp.2020.105533 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Precipitable water Type: general – SubjectFull: Water vapor Type: general – SubjectFull: Rainfall Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Global modeling systems Type: general – SubjectFull: Tehran (Iran) Type: general Titles: – TitleFull: Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khaniani, Ali Sam – PersonEntity: Name: NameFull: Motieyan, Hamid – PersonEntity: Name: NameFull: Mohammadi, Atefeh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13646826 Numbering: – Type: volume Value: 214 Titles: – TitleFull: Journal of Atmospheric & Solar-Terrestrial Physics Type: main |
| ResultId | 1 |
