• Abramovici, M., , Neubach M. , , Fathi M. , , and Holland A. , 2008: Competing fusion for Bayesian applications. Proceedings of IPMU 2008: 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, L. Magdalena, J.-L. Verdegay, and M. Ojeda-Aciego, Eds., Springer, 378385.

  • Arkin, P. A., , and Ardanuy P. E. , 1989: Estimating climatic-scale precipitation from space: A review. J. Climate, 2, 12291238, doi:10.1175/1520-0442(1989)002<1229:ECSPFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Arnaud, P., , Fine J. A. , , and Lavabre J. , 2007: An hourly rainfall generation model applicable to all types of climate. Atmos. Res., 85, 230242, doi:10.1016/j.atmosres.2007.01.002.

    • Search Google Scholar
    • Export Citation
  • Asklany, S. A., , Elhelow K. , , Youssef I. K. , , and El-wahab M. A. , 2011: Rainfall events prediction using rule-based fuzzy inference system. Atmos. Res., 101, 228236, doi:10.1016/j.atmosres.2011.02.015.

    • Search Google Scholar
    • Export Citation
  • Bai, C. G., 2005: Bayesian network based software reliability prediction with an operational profile. J. Syst. Software, 77, 103112, doi:10.1016/j.jss.2004.11.034.

    • Search Google Scholar
    • Export Citation
  • Benitez, J. M., , Castro J. L. , , and Requena I. , 1997: Are artificial neural networks black boxes? IEEE Trans. Neural Network, 8, 11561164, doi:10.1109/72.623216.

    • Search Google Scholar
    • Export Citation
  • Bouckaert, R. R., , Frank E. , , Hall M. , , Kirkby R. , , Reutemann P. , , Seewald A. , , and Scuse D. , 2010: WEKA manual for version 3-7-3. University of Waikato, 325 pp.

  • Buntine, W. L., 1996: A guide to the literature on learning probabilistic networks from data. IEEE Trans. Knowl. Data Eng., 8, 195210, doi:10.1109/69.494161.

    • Search Google Scholar
    • Export Citation
  • Cerquides, J., , and Màntaras R. Lòpez de , 2004: Maximum a posteriori tree augmented naïve Bayes classifiers. Discovery Science: Seventh International Conference, Lecture Notes in Computer Science, Vol. 3245, Springer Berlin Heidelberg, 73–88.

  • Chau, K., , Wu C. , , and Li Y. , 2005: Comparison of several flood forecasting models in Yangtze River. J. Hydrol. Eng., 10, 485491, doi:10.1061/(ASCE)1084-0699(2005)10:6(485).

    • Search Google Scholar
    • Export Citation
  • Chen, Y.-S., , Hung Y.-P. , , Yen T.-F. , , and Fuh C.-S. , 2007: Fast and versatile algorithm for nearest neighbor search based on a lower bound tree. Pattern Recognit., 40, 360375, doi:10.1016/j.patcog.2005.08.016.

    • Search Google Scholar
    • Export Citation
  • Cheng, C.-C., , Hsu N.-S. , , and Wei C.-C. , 2008: Decision-tree analysis on optimal release of reservoir storage under typhoon warnings. Nat. Hazards, 44, 6584, doi:10.1007/s11069-007-9142-1.

    • Search Google Scholar
    • Export Citation
  • Cheng, J., , and Greiner R. , 1999: Comparing Bayesian network classifiers. Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference, K. B. Laskey and H. Prade, Eds., Morgan Kaufmann Publishers, 101107.

  • Chow, V. T., , Maidment D. R. , , and Mays L. W. , 1988: Applied Hydrology. McGraw-Hill, 572 pp.

  • Cooper, G., , and Herskovits E. , 1992: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn., 9, 309347, doi:10.1007/BF00994110.

    • Search Google Scholar
    • Export Citation
  • Daly, R., , Shena Q. , , and Aitken S. , 2011: Learning Bayesian networks: Approaches and issues. Knowl. Eng. Rev., 26, 99157, doi:10.1017/S0269888910000251.

    • Search Google Scholar
    • Export Citation
  • Demichelis, F., , Magni P. , , Piergiorgi P. , , Rubin M. A. , , and Bellazzi R. , 2006: A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: An application to tissue microarrays. BMC Bioinf., 7, 514, doi:10.1186/1471-2105-7-514.

    • Search Google Scholar
    • Export Citation
  • Duda, R., , and Hart P. , 1973: Pattern Classification and Scene Analysis. John Wiley & Sons, 482 pp.

  • Ferraro, R. R., , and Marks G. F. , 1995: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J. Atmos. Oceanic Technol., 12, 755770, doi:10.1175/1520-0426(1995)012<0755:TDOSRR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., , Grody N. C. , , and Marks G. F. , 1994: Effects of surface conditions on rain identification using the SSM/I. Remote Sens. Rev., 11, 195209, doi:10.1080/02757259409532265.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., , Weng F. , , Grody N. C. , , and Basist A. , 1996: An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc., 77, 891905, doi:10.1175/1520-0477(1996)077<0891:AEYTSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Friedman, N., , Geiger D. , , and Goldszmidt M. , 1997: Bayesian network classifiers. Mach. Learn., 29, 131163, doi:10.1023/A:1007465528199.

    • Search Google Scholar
    • Export Citation
  • Gelman, A., , Carlin J. B. , , Stern H. S. , , and Rubin D. B. , 2004: Bayesian Data Analysis. 2nd ed. Chapman & Hall, 668 pp.

  • Gopnik, A., , and Tenenbaum J. B. , 2007: Bayesian networks, Bayesian learning and cognitive development. Dev. Sci., 10, 281287, doi:10.1111/j.1467-7687.2007.00584.x.

    • Search Google Scholar
    • Export Citation
  • Greiner, M., , Pfeiffer D. , , and Smith R. D. , 2000: Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med., 45, 2341, doi:10.1016/S0167-5877(00)00115-X.

    • Search Google Scholar
    • Export Citation
  • Hanley, J. A., , and McNeil B. J. , 1982: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 2936, doi:10.1148/radiology.143.1.7063747.

    • Search Google Scholar
    • Export Citation
  • Heckerman, D., 1995: A tutorial on learning Bayesian networks. Microsoft Research Tech. Rep. MSR-TR-95-06, 57 pp. [Available online at http://research.microsoft.com/en-us/um/people/heckerman/tutorial.pdf.]

  • Hosmer, D. W., , and Lemeshow S. , 2000: Applied Logistic Regression. 2nd ed. John Wiley & Sons, 373 pp.

  • Hsu, C.-C., , Huang Y.-P. , , and Chang K.-W. , 2008: Extended Naïve Bayes classifier for mixed data. Expert Syst. Appl., 35, 10801083, doi:10.1016/j.eswa.2007.08.031.

    • Search Google Scholar
    • Export Citation
  • Islam, T., , Rico-Ramirez M. A. , , Srivastava P. K. , , and Dai Q. , 2014: Non-parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19–85 GHz channels with the Random Forests algorithm. Int. J. Remote Sens., 35, 32543267, doi:10.1080/01431161.2014.903444.

    • Search Google Scholar
    • Export Citation
  • Kurt, I., , Ture M. , , and Kurum A. T. , 2008: Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst. Appl., 34, 366374, doi:10.1016/j.eswa.2006.09.004.

    • Search Google Scholar
    • Export Citation
  • Langley, P., , Iba W. , , and Thompson K. , 1992: An analysis of Bayesian classifiers. AAAI-92: Proceedings of the 10th National Conference on Artificial Intelligence, AAAI Press, 223228.

  • Lauritzen, S. L., 1996: Graphical Models. Oxford University Press, 298 pp.

  • Lee, C.-Y., 2005: Application of rainfall frequency analysis on studying rainfall distribution characteristics of Chia-Nan Plain Area in Southern Taiwan. Crop Environ. Bioinf., 2, 3138.

    • Search Google Scholar
    • Export Citation
  • Liguori, S., , Rico-Ramirez M. A. , , Schellart A. N. A. , , and Saul A. J. , 2012: Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmos. Res., 103, 8095, doi:10.1016/j.atmosres.2011.05.004.

    • Search Google Scholar
    • Export Citation
  • Lin, G.-F., , and Wu M.-C. , 2009: A hybrid neural network model for typhoon-rainfall forecasting. J. Hydrol., 375, 450458, doi:10.1016/j.jhydrol.2009.06.047.

    • Search Google Scholar
    • Export Citation
  • Lin, J. Y., , Cheng C. T. , , and Chau K. W. , 2006: Using support vector machines for long-term discharge prediction. Hydrol. Sci. J., 51, 599612, doi:10.1623/hysj.51.4.599.

    • Search Google Scholar
    • Export Citation
  • Liu, G., , and Curry J. A. , 1992: Retrieval of precipitation from satellite microwave measurement using both emission and scattering. J. Geophys. Res., 97, 99599974, doi:10.1029/92JD00289.

    • Search Google Scholar
    • Export Citation
  • Liu, J. T., , Chao S.-Y. , , and Hsu R. T. , 2002: Numerical modeling study of sediment dispersal by a river plume. Cont. Shelf Res., 22, 17451773, doi:10.1016/S0278-4343(02)00036-5.

    • Search Google Scholar
    • Export Citation
  • Lucas, P. J. F., 2005: Bayesian network modelling through qualitative patterns. Artif. Intell., 163, 233263, doi:10.1016/j.artint.2004.10.011.

    • Search Google Scholar
    • Export Citation
  • Mahesh, C., , Prakash S. , , Sathiyamoorthy V. , , and Gairola R. M. , 2011: Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature. Atmos. Res., 102, 358364, doi:10.1016/j.atmosres.2011.09.003.

    • Search Google Scholar
    • Export Citation
  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291303.

  • McBride, J. L., , and Ebert E. E. , 2000: Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia. Wea. Forecasting, 15, 103121, doi:10.1175/1520-0434(2000)015<0103:VOQPFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Munot, A. A., , and Kumar K. K. , 2007: Long range prediction of Indian summer monsoon rainfall. J. Earth Syst. Sci., 116, 7379, doi:10.1007/s12040-007-0008-4.

    • Search Google Scholar
    • Export Citation
  • Nadkarni, S., , and Shenoy P. P. , 2001: A Bayesian network approach to making inferences in causal maps. Eur. J. Oper. Res., 128, 479498, doi:10.1016/S0377-2217(99)00368-9.

    • Search Google Scholar
    • Export Citation
  • Nayagam, L. R., , Janardanan R. , , and Mohan H. S. R. , 2008: An empirical model for the seasonal prediction of southwest monsoon rainfall over Kerala, a meteorological subdivision of India. Int. J. Climatol., 28, 823831, doi:10.1002/joc.1577.

    • Search Google Scholar
    • Export Citation
  • Pearl, J., 1988: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 552 pp.

  • Pérez, A., , Larranaga P. , , and Inza I. , 2006: Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naïve Bayes. Int. J. Approximate Reasoning, 43, 125, doi:10.1016/j.ijar.2006.01.002.

    • Search Google Scholar
    • Export Citation
  • Pernkopf, F., 2005: Bayesian network classifiers versus selective k-NN classifier. Pattern Recognit., 38, 110, doi:10.1016/j.patcog.2004.05.012.

    • Search Google Scholar
    • Export Citation
  • Petty, G. W., , and Krajewski W. F. , 1996: Satellite estimation of precipitation over land. Hydrol. Sci. J., 41, 433451, doi:10.1080/02626669609491519.

    • Search Google Scholar
    • Export Citation
  • Petty, G. W., , and Li K. , 2013: Improved passive microwave retrievals of rain rate over land and ocean. Part I: Algorithm description. J. Atmos. Oceanic Technol., 30, 24932508, doi:10.1175/JTECH-D-12-00144.1.

    • Search Google Scholar
    • Export Citation
  • Rijmen, F., 2008: Bayesian networks with a logistic regression model for the conditional probabilities. Int. J. Approximate Reasoning, 48, 659666, doi:10.1016/j.ijar.2008.01.001.

    • Search Google Scholar
    • Export Citation
  • Saheli, E., , and Gras R. , 2009: An empirical comparison of the efficiency of several local search heuristics algorithms for Bayesian network structure learning. Learning and Intelligent Optimization (LION 3), Trento, Italy, IEEE, 13 pp. [Available online at http://www.intelligent-optimization.org/LION3/online_proceedings/72.pdf.]

  • Seco, A., and et al. , 2012: Rain pattern analysis and forecast model based on GPS estimated atmospheric water vapor content. Atmos. Environ., 49, 8593, doi:10.1016/j.atmosenv.2011.12.019.

    • Search Google Scholar
    • Export Citation
  • Talei, A., , Chua L. H. C. , , and Wong T. S. W. , 2010: Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J. Hydrol., 391, 248262, doi:10.1016/j.jhydrol.2010.07.023.

    • Search Google Scholar
    • Export Citation
  • Toth, E., , Brath A. , , and Montanari A. , 2000: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol., 239, 132147, doi:10.1016/S0022-1694(00)00344-9.

    • Search Google Scholar
    • Export Citation
  • Trömel, S., , Ziegert M. , , Ryzhkov A. V. , , Chwala C. , , and Simmer C. , 2014: Using microwave backhaul links to optimize the performance of algorithms for rainfall estimation and attenuation correction. J. Atmos. Oceanic Technol., 31, 17481760, doi:10.1175/JTECH-D-14-00016.1.

    • Search Google Scholar
    • Export Citation
  • Wang, W. C., , Cheng C. T. , , Chau K. W. , , and Xu D. M. , 2012: Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model. J. Hydroinf., 14, 784799, doi:10.2166/hydro.2011.027.

    • Search Google Scholar
    • Export Citation
  • Wei, C.-C., 2012: Wavelet support vector machines for forecasting precipitations in tropical cyclones: Comparisons with GSVM, regressions, and numerical MM5 model. Wea. Forecasting, 27, 438450, doi:10.1175/WAF-D-11-00004.1.

    • Search Google Scholar
    • Export Citation
  • Wei, C.-C., , and Hsu N.-S. , 2009: Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. J. Hydrol., 365, 213224, doi:10.1016/j.jhydrol.2008.11.038.

    • Search Google Scholar
    • Export Citation
  • Wei, C.-C., , and Lu Y. H. , 2012: Nearest neighbor search for diagnosing rain/non-rain discrimination. Adv. Mater. Res., 599, 664668, doi:10.4028/www.scientific.net/AMR.599.664.

    • Search Google Scholar
    • Export Citation
  • Wei, C.-C., , and Roan J. , 2012: Retrievals for the rainfall rate over land using Special Sensor Microwave/Imager data during tropical cyclones: Comparisons of scattering index, regression, and support vector regression. J. Hydrometeor., 13, 15671578, doi:10.1175/JHM-D-11-0118.1.

    • Search Google Scholar
    • Export Citation
  • Wei, C.-C., , Hsu N.-S. , , and Huang C.-L. , 2014: Two-stage pumping control model for flood mitigation in inundated urban drainage basins. Water Resour. Manage., 28, 425444, doi:10.1007/s11269-013-0491-0.

    • Search Google Scholar
    • Export Citation
  • Wiggins, M., , Saad A. , , Litt B. , , and Vachtsevanos G. , 2008: Evolving a Bayesian classifier for ECG-based age classification in medical applications. Appl. Soft Comput., 8, 599608, doi:10.1016/j.asoc.2007.03.009.

    • Search Google Scholar
    • Export Citation
  • Witten, I. H., , and Frank E. , 2000: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 371 pp.

  • Wong, M. L., , Lee S. Y. , , and Leung K. S. , 2004: Data mining of Bayesian networks using cooperative coevolution. Decis. Support Syst., 38, 451472, doi:10.1016/S0167-9236(03)00115-5.

    • Search Google Scholar
    • Export Citation
  • Wu, C. L., , Chau K. W. , , and Li Y. S. , 2008: River stage prediction based on a distributed support vector regression. J. Hydrol., 358, 96111, doi:10.1016/j.jhydrol.2008.05.028.

    • Search Google Scholar
    • Export Citation
  • Zou, K. H., 2001: Comparison of correlated receiver operating characteristic curves derived from repeated diagnostic test data. Acad. Radiol., 8, 225233, doi:10.1016/S1076-6332(03)80531-7.

    • Search Google Scholar
    • Export Citation
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Diagnosing Rain Occurrences Using Passive Microwave Imagery: A Comparative Study on Probabilistic Graphical Models and “Black Box” Models

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  • 1 Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
  • | 2 Department of Civil Engineering, National Taiwan University, Taipei, Taiwan
  • | 3 Department of Civil Engineering, Chung Hua University, Hsinchu, Taiwan
  • | 4 Department of Shipping Technology, National Kaohsiung Marine University, Kaohsiung, Taiwan
  • | 5 Department of Information Management, National Chung Cheng University, Chiayi, Taiwan
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Abstract

Rainfall is a fundamental process in the hydrologic cycle. This study investigated the cause–effect relationship in which precipitation at lower frequencies affects the amount of emitted radiation and at higher frequencies affects the amount of backscattered terrestrial radiation. Because the advantage of a probabilistic graphical model is its graphical representation, which allows easy causality interpretation using the arc directions, two Bayesian networks (BNs) were used, namely, a naïve Bayes classifier and a tree-augmented naïve Bayes model. To empirically evaluate and compare BN-based models, “black box”–based models, including nearest-neighbor searches and artificial neural network (ANN)-based multilayer perceptron and logistic regression, were used as benchmarks. For the two study regions—namely, the Tanshui River basin in northern Taiwan and Chianan Plain in southern Taiwan—rain occurrences during typhoon seasons were examined using passive microwave imagery recorded using the Special Sensor Microwave Imager/Sounder. The results show that although black box models exhibit excellent prediction ability, interpretation of their behavior is unsatisfactory. By contrast, probabilistic graphical models can explicitly reveal the causal relationship between brightness temperatures and nonrain/rain discrimination. For the Tanshui River basin, 19.35-, 22.23-, 37.0-, and 85.5-GHz vertically polarized brightness temperatures were found to diagnose rain occurrences. For the Chianan Plain, a more sensitive indicator of rain-scattering signals was obtained using 85-GHz measurements. The results demonstrate the potential use of BNs in identifying rain occurrences in regions with land features comprising various absorbing and scattering materials.

Corresponding author address: Chih-Chiang Wei, Department of Marine Environmental Informatics, National Taiwan Ocean University, No. 2, Beining Rd., Jhongjheng District, Keelung 20224, Taiwan. E-mail: ccwei@ntou.edu.tw; genejyu@ntu.edu.tw; lichen@chu.edu.tw; ccchou@mail.nkmu.edu.tw; bmajsr@ccu.edu.tw

Abstract

Rainfall is a fundamental process in the hydrologic cycle. This study investigated the cause–effect relationship in which precipitation at lower frequencies affects the amount of emitted radiation and at higher frequencies affects the amount of backscattered terrestrial radiation. Because the advantage of a probabilistic graphical model is its graphical representation, which allows easy causality interpretation using the arc directions, two Bayesian networks (BNs) were used, namely, a naïve Bayes classifier and a tree-augmented naïve Bayes model. To empirically evaluate and compare BN-based models, “black box”–based models, including nearest-neighbor searches and artificial neural network (ANN)-based multilayer perceptron and logistic regression, were used as benchmarks. For the two study regions—namely, the Tanshui River basin in northern Taiwan and Chianan Plain in southern Taiwan—rain occurrences during typhoon seasons were examined using passive microwave imagery recorded using the Special Sensor Microwave Imager/Sounder. The results show that although black box models exhibit excellent prediction ability, interpretation of their behavior is unsatisfactory. By contrast, probabilistic graphical models can explicitly reveal the causal relationship between brightness temperatures and nonrain/rain discrimination. For the Tanshui River basin, 19.35-, 22.23-, 37.0-, and 85.5-GHz vertically polarized brightness temperatures were found to diagnose rain occurrences. For the Chianan Plain, a more sensitive indicator of rain-scattering signals was obtained using 85-GHz measurements. The results demonstrate the potential use of BNs in identifying rain occurrences in regions with land features comprising various absorbing and scattering materials.

Corresponding author address: Chih-Chiang Wei, Department of Marine Environmental Informatics, National Taiwan Ocean University, No. 2, Beining Rd., Jhongjheng District, Keelung 20224, Taiwan. E-mail: ccwei@ntou.edu.tw; genejyu@ntu.edu.tw; lichen@chu.edu.tw; ccchou@mail.nkmu.edu.tw; bmajsr@ccu.edu.tw
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