Modeling of Precipitable Water Vapor Using an Adaptive Neuro-Fuzzy Inference System in the Absence of the GPS Network

Wayan Suparta Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor Darul Ehsan, Malaysia

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Kemal Maulana Alhasa Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor Darul Ehsan, Malaysia

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Abstract

This paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, Elman neural networks, and MLR, with a mean absolute error of 0.015 mm, a percent error of 0.028%, and root-mean-square error of 0.019 mm. These results suggest that the ANFIS model is a promising approach for estimating PWV values that is cost effective, continuous, and potentially useful for meteorological applications.

Corresponding author address: Wayan Suparta, ANGKASA, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia. E-mail: wayan@ukm.edu.my

Abstract

This paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, Elman neural networks, and MLR, with a mean absolute error of 0.015 mm, a percent error of 0.028%, and root-mean-square error of 0.019 mm. These results suggest that the ANFIS model is a promising approach for estimating PWV values that is cost effective, continuous, and potentially useful for meteorological applications.

Corresponding author address: Wayan Suparta, ANGKASA, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia. E-mail: wayan@ukm.edu.my
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  • Ao, S. I., and V. Palade, 2011: Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks. Appl. Soft Comput., 11, 17181726, doi:10.1016/j.asoc.2010.05.014.

    • Search Google Scholar
    • Export Citation
  • Arabacioglu, B. C., 2010: Using fuzzy inference system for architectural space analysis. Appl. Soft Comput., 10, 926937, doi:10.1016/j.asoc.2009.10.011.

    • Search Google Scholar
    • Export Citation
  • Badr, H. S., B. F. Zaitchik, and S. D. Guikema, 2014: Application of statistical models to the prediction of seasonal rainfall anomalies over the Sahel. J. Appl. Meteor. Climatol., 53, 614636, doi:10.1175/JAMC-D-13-0181.1.

    • Search Google Scholar
    • Export Citation
  • Bae, D. H., D. M. Jeong, and G. Kim, 2007: Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrol. Sci. J., 52, 99113, doi:10.1623/hysj.52.1.99.

    • Search Google Scholar
    • Export Citation
  • Bashari, A., M. Beiki, and A. Talebinejad, 2011: Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling. Int. J. Rock Mech. Min. Sci., 48, 12241234, doi:10.1016/j.ijrmms.2011.09.017.

    • Search Google Scholar
    • Export Citation
  • Bevis, M., S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, 1992: GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res., 97, 15 78715 801, doi:10.1029/92JD01517.

    • Search Google Scholar
    • Export Citation
  • Bevis, M., S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, 1994: GPS meteorology: Mapping zenith wet delay onto precipitable water. J. Appl. Meteor., 33, 379386, doi:10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bishop, M., 1995: Neural Networks for Pattern Recognition. Oxford University Press, 504 pp.

  • Boyacioglu, M. A., and D. Avci, 2010: An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange. Expert Syst. Appl., 37, 79087912, doi:10.1016/j.eswa.2010.04.045.

    • Search Google Scholar
    • Export Citation
  • Buckley, J. J., E. Eslami, and T. Feuring, 2002: Fuzzy Mathematics in Economics and Engineering. Studies in Fuzziness and Soft Computing, Vol. 91, Physica, 271 pp.

  • Campmany, E., J. Bech, J. Rodríguez-Marcos, Y. Sola, and J. Lorente, 2010: A comparison of total precipitable water measurements from radiosonde and sunphotometers. Atmos. Res., 97, 385392, doi:10.1016/j.atmosres.2010.04.016.

    • Search Google Scholar
    • Export Citation
  • Chan, A. L. S., 2011: Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energy Build., 43, 28602868, doi:10.1016/j.enbuild.2011.07.003.

    • Search Google Scholar
    • Export Citation
  • Chaudhuri, S., and A. Middey, 2011: Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms. Meteor. Atmos. Phys., 114, 139149, doi:10.1007/s00703-011-0158-4.

    • Search Google Scholar
    • Export Citation
  • Chen, B., and Z. Liu, 2014: Analysis of precipitable water vapor (PWV) data derived from multiple techniques: GPS, WVR, radiosonde and NHM in Hong Kong. Proceedings of the China Satellite Navigation Conference (CSNC) 2014, Vol. I, J. Sun et al., Eds., Lecture Notes in Electrical Engineering, Vol. 303, Springer, 159175, doi:10.1007/978-3-642-54737-9_16.

  • Chiu, S. L., 1994: Fuzzy model identification based on clustering estimation. J. Intell. Fuzzy Syst., 2, 267278, doi:10.3233/IFS-1994-2306.

    • Search Google Scholar
    • Export Citation
  • Dogan, E., M. Gumrukcuoglu, M. Sandalci, and M. Opan, 2010: Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems. Eng. Appl. Artif. Intell., 23, 961967, doi:10.1016/j.engappai.2010.03.007.

    • Search Google Scholar
    • Export Citation
  • Garcia, P., C. A. Garcia, L. M. Fernandez, F. Llorens, and F. Jurado, 2014: ANFIS-based control of a grid-connected hybrid system integrating renewable energies, hydrogen and batteries. IEEE Trans. Ind. Inf., 10, 11071117, doi:10.1109/TII.2013.2290069.

    • Search Google Scholar
    • Export Citation
  • Ghosh, S., 2010: SVM‐PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. J. Geophys. Res., 115, D22102, doi:10.1029/2009JD013548.

    • Search Google Scholar
    • Export Citation
  • Gong, M., Y. Liang, J. Shi, W. Ma, and J. Ma, 2013: Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process., 22, 573584, doi:10.1109/TIP.2012.2219547.

    • Search Google Scholar
    • Export Citation
  • Gordon, N. D., A. K. Jonko, P. M. Forster, and K. M. Shell, 2013: An observationally based constraint on the water-vapor feedback. J. Geophys. Res. Atmos., 118, 12 43512 443, doi:10.1002/2013JD020184.

    • Search Google Scholar
    • Export Citation
  • He, Z., X. Wen, H. Liu, and J. Du, 2014: A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J. Hydrol., 509, 379386, doi:10.1016/j.jhydrol.2013.11.054.

    • Search Google Scholar
    • Export Citation
  • Jang, J. S. R., 1993: ANFIS: Adaptive network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern., 23, 665685, doi:10.1109/21.256541.

    • Search Google Scholar
    • Export Citation
  • Jang, J. S. R., and C. T. Sun, 1995: Neuro-fuzzy modeling and control. Proc. IEEE, 83, 378406, doi:10.1109/5.364486.

  • Kealy, J., J. Foster, and S. Businger, 2012: GPS meteorology: An investigation of ocean-based precipitable water estimates. J. Geophys. Res., 117, D17303, doi:10.1029/2011JD017422.

    • Search Google Scholar
    • Export Citation
  • Krinidis, S., and V. Chatzis, 2010: A robust fuzzy local information c-means clustering algorithm. IEEE Trans. Image Process., 19, 13281337, doi:10.1109/TIP.2010.2040763.

    • Search Google Scholar
    • Export Citation
  • Lee, S. W., J. Kouba, B. Schutz, D. H. Kim, and Y. J. Lee, 2013: Monitoring precipitable water vapor in real-time using global navigation satellite systems. J. Geod., 87, 923934, doi:10.1007/s00190-013-0655-y.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., C. Yonggi, and L. Jingnan, 2000: Remote sensing of atmospheric water vapor using ground-based GPS data. Geo-Spatial Inf. Sci., 3, 6468, doi:10.1007/BF02826612.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., M. Li, W. Zhong, and M. S. Wong, 2013: An approach to evaluate the absolute accuracy of WVR water vapor measurements inferred from multiple water vapor techniques. J. Geodyn., 72, 8694, doi:10.1016/j.jog.2013.09.002.

    • Search Google Scholar
    • Export Citation
  • Mekanik, F., M. A. Imteaz, and A. Talei, 2015: Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Climate Dyn., 46, 30973111, doi:10.1007/s00382-015-2755-2.

    • Search Google Scholar
    • Export Citation
  • Nayak, P. C., K. P. Sudheer, and S. K. Jain, 2014: River flow forecasting through nonlinear local approximation in a fuzzy model. Neural Comput. Appl., 25, 19511965, doi:10.1007/s00521-014-1684-z.

    • Search Google Scholar
    • Export Citation
  • Ning, T., R. Haas, G. Elgered, and U. Willén, 2012: Multi-technique comparisons of 10 years of wet delay estimates on the west coast of Sweden. J. Geod., 86, 565575, doi:10.1007/s00190-011-0527-2.

    • Search Google Scholar
    • Export Citation
  • Nourani, V., and M. Komasi, 2013: A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process. J. Hydrol., 490, 4155, doi:10.1016/j.jhydrol.2013.03.024.

    • Search Google Scholar
    • Export Citation
  • Opaluwa, Y. D., T. A. Musa, A. H. Omar, M. D. Subari, and L. M. Ojigi, 2014: Preliminary design for near real-time GPS meteorology over Peninsular Malaysia (G-MeM). Terr. Atmos. Oceanic Sci., 25, 813826, doi:10.3319/TAO.2014.08.04.01(A).

    • Search Google Scholar
    • Export Citation
  • Park, H. J., J. G. Um, I. Woo, and J. W. Kim, 2012: Application of fuzzy set theory to evaluate the probability of failure in rock slopes. Eng. Geol., 125, 92101, doi:10.1016/j.enggeo.2011.11.008.

    • Search Google Scholar
    • Export Citation
  • Park, J., J. W. Kim, J. H. Chang, Y. G. Jin, and N. S. Kim, 2015: Estimation of speech absence uncertainty based on multiple linear regression analysis for speech enhancement. Appl. Acoust., 87, 205211, doi:10.1016/j.apacoust.2014.06.017.

    • Search Google Scholar
    • Export Citation
  • Peteiro-Barral, D., and B. Guijarro-Berdiñas, 2013: A study on the scalability of artificial neural networks training algorithms using multiple-criteria decision-making methods. Artificial Intelligence and Soft Computing, L. Rutkowski et al., Eds., Lecture Notes in Computer Science, Vol. 7894, Springer, 162–173.

  • Rocken, C., T. V. Hove, and R. Ware, 1997: Near real-time GPS sensing of atmospheric water vapor. Geophys. Res. Lett., 24, 32213224, doi:10.1029/97GL03312.

    • Search Google Scholar
    • Export Citation
  • Roth, I., and M. Margaliot, 2010: Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach. Fuzzy Sets Syst., 161, 25692584, doi:10.1016/j.fss.2010.04.007.

    • Search Google Scholar
    • Export Citation
  • Singh, K. K., M. J. Nigam, K. Pal, and A. Mehrotra, 2014: A fuzzy Kohonen local information c-means clustering for remote sensing imagery. IETE Tech. Rev., 31, 7581, doi:10.1080/02564602.2014.891375.

    • Search Google Scholar
    • Export Citation
  • Sugeno, M., and G. T. Kang, 1988: Structure identification of fuzzy model. Fuzzy Sets Syst., 28, 1533, doi:10.1016/0165-0114(88)90113-3.

    • Search Google Scholar
    • Export Citation
  • Suparta, W., 2014: The development of GPS TroWav tool for atmospheric–terrestrial studies. J. Phys. Conf. Ser., 495, 012037, doi:10.1088/1742-6596/495/1/012037.

    • Search Google Scholar
    • Export Citation
  • Suparta, W., and K. M. Alhasa, 2015: Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique. Expert Syst. Appl., 42, 10501064, doi:10.1016/j.eswa.2014.09.029.

    • Search Google Scholar
    • Export Citation
  • Suparta, W., Z. A. Abdul Rashid, M. A. Mohd Ali, B. Yatim, and G. J. Fraser, 2008: Observations of Antarctic precipitable water vapor and its response to the solar activity based on GPS sensing. J. Atmos. Sol.-Terr. Phys., 70, 14191447, doi:10.1016/j.jastp.2008.04.006.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2012: Framing the way to relate climate extremes to climate change. Climatic Change, 115, 283290, doi:10.1007/s10584-012-0441-5.

    • Search Google Scholar
    • Export Citation
  • Wang, B.-R., X.-Y. Liu, and J.-K. Wang, 2013: Assessment of COSMIC radio occultation retrieval product using global radiosonde data. Atmos. Meas. Tech., 6, 10731083, doi:10.5194/amt-6-1073-2013.

    • Search Google Scholar
    • Export Citation
  • Wei, C. C., 2014: Surface wind nowcasting in the Penghu Islands based on classified typhoon tracks and the effects of the central mountain range of Taiwan. Wea. Forecasting, 29, 14251450, doi:10.1175/WAF-D-14-00027.1.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, I., and O. Kaynar, 2011: Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl., 38, 59585966, doi:10.1016/j.eswa.2010.11.027.

    • Search Google Scholar
    • Export Citation
  • Yip, Z. K., and M. K. Yau, 2012: Application of artificial neural networks on North Atlantic tropical cyclogenesis potential index in climate change. J. Atmos. Oceanic Technol., 29, 12021220, doi:10.1175/JTECH-D-11-00178.1.

    • Search Google Scholar
    • Export Citation
  • Yonaba, H., F. Anctil, and V. Fortin, 2010: Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J. Hydrol. Eng., 15, 275283, doi:10.1061/(ASCE)HE.1943-5584.0000188.

    • Search Google Scholar
    • Export Citation
  • Yu, H., and B. M. Wilamowski, 2011: Levenberg–Marquardt training. Industrial Electronics Handbook, Vol. 5, B. M. Wilamowski and J. D. Irwin, Eds., CRC Press, 12-1–12-15.

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