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Gaussian Naïve Bayes Classification Algorithm for Drought and Flood Risk Reduction

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Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation

Abstract

There exist general speculations and scientifically proven indications that climate-induced hazards will increase due to climate change and large-scale variability. Consequently, intelligent data analytic models are increasingly harnessed as decision support tools. This chapter presents an intelligent data analytic technique for predicting several magnitudes of droughts and floods, as well as conditions that necessitate their occurrence based on independent atmospheric variables of North Central Nigeria. The chapter builds knowledge on the Gaussian Naïve Bayes classification. Monthly climate data (1960–2015) which consists of rainfall, maximum and minimum temperature, relative humidity, wind speed, sunshine duration, potential evapotranspiration, and Standardized Precipitation Index (SPI) were employed as predictor variables, while seven SPI classes were employed as the response variable. Seventy percent (70%) of the dataset was used to train the models while the remaining 30% was used as test dataset. Furthermore, the performance of the developed models was evaluated using statistical techniques and confusion matrix. The developed models show that extreme dry, severe dry, moderate dry, near normal, moderate wet, severe wet, and extreme wet events had probability of occurrence of 0.02, 0.04, 0.05, 0.70, 0.05, 0.04, 0.02, respectively, while the chapter further revealed that Gaussian Naïve Bayes model having SPI values as one of its predictors performed better than the model without SPI values, with correctly classified instances of 92.9% and 65.7%, respectively. In addition, based on the well-suited model, it was unraveled that rainfall of 45.2 mm ± 25.9, maximum temperature of 31.5 °C ± 1.8, minimum temperature of 22.0 °C ± 1.1, relative humidity of 79.5 ± 4.4, evaporation of 4.0 ± 2, sunshine hour of 6.4 h ± 1.2, wind speed of 4.1 ms−1 ± 1.0, potential evapotranspiration of 151.6 mm ± 21.4, and SPI of -2.4 ± 0.4, necessitate the occurrence of extreme droughts while rainfall of 236.4 mm ± 149.2, maximum temperature of 33.2 °C ± 1.9, minimum temperature of 21.05 °C ± 1.1, relative humidity of 77.6 ± 8.5, evaporation of 4.6 + 2.5, sunshine hour of 6.8 h ± 0.8, wind speed of 4.0 ms−1 ± 1.0, potential evapotranspiration of 164.3 mm ± 15.4, and SPI of 2.4 ± 0.3 necessitate extreme floods. Consequently, the established data intelligent analytic model would be useful to stakeholders interested in reducing the risks of natural hazards. This chapter concludes that the predictive performance of the Gaussian Naïve Bayes classification model in predicting climate-induced hazards is sensitive to the predictor variables employed.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Ibadan, Ibadan, Nigeria

    Oluwatobi Aiyelokun, Akintunde Ojelabi & Oluwole Agbede

  2. Department of Computer and Information Science, Tai Solarin University of Education, Ijebu-Ode, Ogun, Nigeria

    Gbenga Ogunsanwo

Authors
  1. Oluwatobi Aiyelokun
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  2. Gbenga Ogunsanwo
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  3. Akintunde Ojelabi
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  4. Oluwole Agbede
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Correspondence to Oluwatobi Aiyelokun .

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Editors and Affiliations

  1. School of Sciences, University of Southern Queensland, Springfield Central, QLD, Australia

    Ravinesh C. Deo

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  4. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Zaher Mundher Yaseen

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Aiyelokun, O., Ogunsanwo, G., Ojelabi, A., Agbede, O. (2021). Gaussian Naïve Bayes Classification Algorithm for Drought and Flood Risk Reduction. In: Deo, R., Samui, P., Kisi, O., Yaseen, Z. (eds) Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5772-9_3

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