The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah

Wayan Suparta, Wahyu Sasongko Putro, Mandeep Singh Jit Singh, Mhd Fairos Asillam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For succeed the early warning system development programme for space activity plan at Tawau, Sabah Malaysia, we studied the variation of rainfall and precipitation over convective system activity. We use five variables data such as the surface meteorological data (Pressure, Temperature and Relative Humidity), rainfall data, and precipitation. The surface meteorological data are taken from weather underground website, the rainfall data we obtain from rain gauge sensor Department of Irrigation and Drainage (DID) Malaysia, and also the precipitation data was obtained from NASA Satellite (Tropical Rainfall Measuring Mission). In this study, we attempt to estimate two variable targets (rainfall and precipitation) using Artificial Neural Network (ANN). The estimation was constructed as an alternative method to develop rainfall and precipitation model using surface meteorological data during convective system activity on summer monsoon over Tawau area. In this study, we processed surface meteorological, rainfall and precipitation data from 1 June 2011 to 31 August 2011. The Multilayer Perceptron (MLP) architecture and Levenberg Marquardt (LM) algorithms have been deployed in this study. We obtain the good result of estimation with one output target. The Root Mean Square Error (RMSE) value of rainfall and precipitation were 4.3540e-004 and 2.7834e-004 respectively and also Variance Accounted For (VAF) of rainfall and precipitation were 94.344% and 98.923% respectively during the training process. Testing results showed an error of 5.656% and 1.077% for precipitation and rainfall, respectively. However, we found the weakness of estimation model using three parameter inputs and two parameter output targets with the same structure. It means that for future work we suggest using 3 years data over convective system activity during summer monsoon is considered in the output target (rainfall and precipitation).

Original languageEnglish
Title of host publicationInternational Conference on Space Science and Communication, IconSpace
PublisherIEEE Computer Society
Pages479-484
Number of pages6
Volume2015-September
ISBN (Print)9781479919406
DOIs
Publication statusPublished - 29 Sep 2015
Event4th International Conference on Space Science and Communication, IconSpace 2015 - Langkawi, Malaysia
Duration: 10 Aug 201512 Aug 2015

Other

Other4th International Conference on Space Science and Communication, IconSpace 2015
CountryMalaysia
CityLangkawi
Period10/8/1512/8/15

Fingerprint

Precipitation (meteorology)
neural network
Rain
Neural networks
Malaysia
early warning system
Rain gages
system development
irrigation
Alarm systems
Multilayer neural networks
website
Irrigation
Mean square error
Drainage
NASA
Websites
Atmospheric humidity
Satellites

Keywords

  • Artificial Neural Network (ANN)
  • Convective System
  • precipitation
  • Rainfall
  • Surface Meteorological
  • Tawau area

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Communication

Cite this

Suparta, W., Putro, W. S., Jit Singh, M. S., & Asillam, M. F. (2015). The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. In International Conference on Space Science and Communication, IconSpace (Vol. 2015-September, pp. 479-484). [7283806] IEEE Computer Society. https://doi.org/10.1109/IconSpace.2015.7283806

The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. / Suparta, Wayan; Putro, Wahyu Sasongko; Jit Singh, Mandeep Singh; Asillam, Mhd Fairos.

International Conference on Space Science and Communication, IconSpace. Vol. 2015-September IEEE Computer Society, 2015. p. 479-484 7283806.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Suparta, W, Putro, WS, Jit Singh, MS & Asillam, MF 2015, The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. in International Conference on Space Science and Communication, IconSpace. vol. 2015-September, 7283806, IEEE Computer Society, pp. 479-484, 4th International Conference on Space Science and Communication, IconSpace 2015, Langkawi, Malaysia, 10/8/15. https://doi.org/10.1109/IconSpace.2015.7283806
Suparta W, Putro WS, Jit Singh MS, Asillam MF. The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. In International Conference on Space Science and Communication, IconSpace. Vol. 2015-September. IEEE Computer Society. 2015. p. 479-484. 7283806 https://doi.org/10.1109/IconSpace.2015.7283806
Suparta, Wayan ; Putro, Wahyu Sasongko ; Jit Singh, Mandeep Singh ; Asillam, Mhd Fairos. / The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. International Conference on Space Science and Communication, IconSpace. Vol. 2015-September IEEE Computer Society, 2015. pp. 479-484
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abstract = "For succeed the early warning system development programme for space activity plan at Tawau, Sabah Malaysia, we studied the variation of rainfall and precipitation over convective system activity. We use five variables data such as the surface meteorological data (Pressure, Temperature and Relative Humidity), rainfall data, and precipitation. The surface meteorological data are taken from weather underground website, the rainfall data we obtain from rain gauge sensor Department of Irrigation and Drainage (DID) Malaysia, and also the precipitation data was obtained from NASA Satellite (Tropical Rainfall Measuring Mission). In this study, we attempt to estimate two variable targets (rainfall and precipitation) using Artificial Neural Network (ANN). The estimation was constructed as an alternative method to develop rainfall and precipitation model using surface meteorological data during convective system activity on summer monsoon over Tawau area. In this study, we processed surface meteorological, rainfall and precipitation data from 1 June 2011 to 31 August 2011. The Multilayer Perceptron (MLP) architecture and Levenberg Marquardt (LM) algorithms have been deployed in this study. We obtain the good result of estimation with one output target. The Root Mean Square Error (RMSE) value of rainfall and precipitation were 4.3540e-004 and 2.7834e-004 respectively and also Variance Accounted For (VAF) of rainfall and precipitation were 94.344{\%} and 98.923{\%} respectively during the training process. Testing results showed an error of 5.656{\%} and 1.077{\%} for precipitation and rainfall, respectively. However, we found the weakness of estimation model using three parameter inputs and two parameter output targets with the same structure. It means that for future work we suggest using 3 years data over convective system activity during summer monsoon is considered in the output target (rainfall and precipitation).",
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