Flood Prediction using Artificial Neural Networks:  Empirical Evidence from Mauritius as a Case Study

A. Zaynah Dhunny; Reena Hansa Seebocus; Zaheer Allam; Mohammad Yasser Chuttur; Muhammed Eltahan; Harsh Mehta

doi:10.17977/um018v3i12020p1-10

Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study

A. Zaynah Dhunny, Reena Hansa Seebocus, Zaheer Allam, Mohammad Yasser Chuttur, Muhammed Eltahan, Harsh Mehta

Abstract

Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features are minimum temperature, maximum temperature, rainfall and humidity and our output decision is „flood‟ or „no flood‟. Using ANN, we achieved an accuracy of 98% for flood prediction and hence, we conclude that ANN is indeed a good predictor for flood occurrence even for regions with predominantly microclimatic conditions.

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References

E. Ashraf, A. Sarwar, M. Junaid, M. B. Baig, H. K. Shurjeel, and R. K. Barrick, “An Assessment of In-service Training Needs for Agricultural Extension Field Staff in the Scenario of Climate Change using Borich Needs Assessment Model.,” Sarhad Journal of Agriculture, vol. 36, no. 2, 2020.

Q. H. Nguyen et al., “Land-use dynamics in the Mekong delta: From national policy to livelihood sustainability,” Sustainable Development, vol. 28, no. 3, pp. 448–467, 2020.

Y. Zhao, X. Zou, L. Cao, Y. Yao, and G. Fu, “Spatiotemporal variations of potential evapotranspiration and aridity index in relation to influencing factors over Southwest China during 1960–2013,” Theoretical and Applied Climatology, vol. 133, no. 3–4, pp. 711–726, 2018.

Z. Allam, “Building a conceptual framework for smarting an existing city in Mauritius: The case of Port Louis,” Journal of Biourbanism, vol. 6, no. 1 & 2, pp. 103–121, 2017.

Z. Allam and D. Jones, “Promoting resilience, liveability and sustainability through landscape architectural design: A conceptual framework for port louis, mauritius; a small island developing state,” in IFLA world congress Singapore, 2018, pp. 1599–1611.

P. Luo et al., “Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions,” Scientific reports, vol. 8, no. 1, pp. 1–11, 2018.

Z. Ahmed, D. R. M. Rao, K. R. M. Reddy, and Y. E. Raj, “Urban Flooding–Case Study of Hyderabad,” Global Journal of Engineering, Design and Technology, vol. 2, no. 4, pp. 63–66, 2013.

M. Narvekar and P. Fargose, “Daily weather forecasting using artificial neural network,” International Journal of computer applications, vol. 121, no. 22, 2015.

R. Nayak, “Artificial Neural Network Model for Weather Prediction,” International Journal of Applied Information System (IJAIS), 2015.

M. P. Darji, V. K. Dabhi, and H. B. Prajapati, “Rainfall forecasting using neural network: A survey,” in 2015 international conference on advances in computer engineering and applications, 2015, pp. 706–713.

G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks:: The state of the art,” International journal of forecasting, vol. 14, no. 1, pp. 35–62, 1998.

A. Tealab, H. Hefny, and A. Badr, “Forecasting of nonlinear time series using ANN,” Future Computing and Informatics Journal, vol. 2, no. 1, pp. 39–47, 2017.

D. R. Nayak, A. Mahapatra, and P. Mishra, “A survey on rainfall prediction using artificial neural network,” International Journal of Computer Applications, vol. 72, no. 16, 2013.

A. Mosavi, P. Ozturk, and K. Chau, “Flood prediction using machine learning models: Literature review,” Water, vol. 10, no. 11, p. 1536, 2018.

B. Ustaoglu, H. Cigizoglu, and M. Karaca, “Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods,” Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling, vol. 15, no. 4, pp. 431–445, 2008.

N. Dempsey, G. Bramley, S. Power, and C. Brown, “The social dimension of sustainable development: Defining urban social sustainability,” Sustainable development, vol. 19, no. 5, pp. 289–300, 2011.

G. A. Fallah-Ghalhary, M. Mousavi-Baygi, and M. Habibi-Nokhandan, “Seasonal rainfall forecasting using artificial neural network,” Journal of Applied Sciences, vol. 9, no. 6, pp. 1098–1105, 2009.

K. Abhishek, A. Kumar, R. Ranjan, and S. Kumar, “A rainfall prediction model using artificial neural network,” 2012 IEEE Control and System Graduate Research Colloquium, Jul. 2012..

K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather forecasting model using artificial neural network,” Procedia Technology, vol. 4, pp. 311–318, 2012.

Mislan, Haviluddin, S. Hardwinarto, Sumaryono, and M. Aipassa, “Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia,” Procedia Computer Science, vol. 59, pp. 142–151, 2015..

C. G. Staub, F. R. Stevens, and P. R. Waylen, “The geography of rainfall in Mauritius: modelling the relationship between annual and monthly rainfall and landscape characteristics on a small volcanic island,” Applied Geography, vol. 54, pp. 222–234, 2014.

C. Mcsweeney, M. New, G. Lizcano, and X. Lu, “The UNDP Climate Change Country Profiles: Improving the accessibility of observed and projected climate information for studies of climate change in developing countries,” Bulletin of the American Meteorological Society, vol. 91, no. 2, pp. 157–166, 2010.

D. Senapathi, F. Underwood, E. Black, M. A. Nicoll, and K. Norris, “Evidence for long-term regional changes in precipitation on the East Coast Mountains in Mauritius,” International Journal of Climatology, vol. 30, no. 8, pp. 1164–1177, 2010.

J. Lee, C.-G. Kim, J. E. Lee, N. W. Kim, and H. Kim, “Application of artificial neural networks to rainfall forecasting in the Geum River basin, Korea,” Water, vol. 10, no. 10, p. 1448, 2018.

C. W. Dawson and R. Wilby, “An artificial neural network approach to rainfall-runoff modelling,” Hydrological Sciences Journal, vol. 43, no. 1, pp. 47–66, 1998.

I. Maqsood, M. R. Khan, and A. Abraham, “An ensemble of neural networks for weather forecasting,” Neural Computing & Applications, vol. 13, no. 2, pp. 112–122, 2004.

A. Kaur, J. K. Sharma, and S. Agrawal, “Artificial neural networks in forecasting maximum and minimum relative humidity,” International Journal of Computer Science and Network Security, vol. 11, no. 5, pp. 197–199, 2011.

C. R. Sunstein, S. Bobadilla-Suarez, S. C. Lazzaro, and T. Sharot, “How people update beliefs about climate change: Good news and bad news,” Cornell L. Rev., vol. 102, p. 1431, 2016.

K. Dube and G. Nhamo, “Evidence and impact of climate change on South African national parks. Potential implications for tourism in the Kruger National Park,” Environmental Development, vol. 33, p. 100485, 2020.

M. P. Byrne and P. A. O‟Gorman, “Trends in continental temperature and humidity directly linked to ocean warming,” Proceedings of the National Academy of Sciences, vol. 115, no. 19, pp. 4863–4868, 2018.

S. Sankaranarayanan, M. Prabhakar, S. Satish, P. Jain, A. Ramprasad, and A. Krishnan, “Flood prediction based on weather parameters using deep learning,” Journal of Water and Climate Change, 2019.

DOI: http://dx.doi.org/10.17977/um018v3i12020p1-10