Abstract
Flooding is, and will likely continue to be, the most catastrophic natural event that undermines successful urban development in the develo** world. Because of their low elevation and proximity to frequent windstorms, coastal communities are particularly more susceptible to natural disasters. The current study uses 40 years of terraclimate data to examine the possibilities of employing machine learning algorithms to predict flood incidences in the capital of Liberia, Monrovia. Two models of Artificial Neuron Networks were selected: Elman Neuron Network, and Cascade Feedforward Neuron Network. The models were trained and tested using two-layer network architecture, different numbers of neurons, and alternating transfer functions (TANSIG and LOGSIG). Five flood-inducing variables: precipitation, windspeed, minimum temperature, maximum temperature, and soil moisture, as input variables. Runoff was chosen as the output variable. The Coefficient of Determination (R2) and Root Mean Square Error (RMSE) were used to test the accuracy of the models. From the results, it was found that the Cascade Feedforward Neuron Network was the most stable during the testing phase, as measured by the statistical coefficient of determination. The CAS-1 model with its ten neurons, two hidden layers, and TANSIG transfer function yielded 51.5% when all five inputs were fed into the network, followed by the Elman-3 model with an R2 score of 37.8%. For the testing phase, the Elman-2 model had a Root Mean Square Error of 0.061516, while the CAS-3 model had a value of 0.068528.
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References
Abatzoglou, J., Dobrowski, S., Parks, S., et al.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018). https://doi.org/10.1038/sdata.2017.191
Cömert, Z., Kocamaz, A.: A study of artificial neural network training algorithms for classification of cardiotocography signals. Bitlis Eren Univ. J. Sci. Technol. 7, 93–103 (2017). https://doi.org/10.17678/beuscitech.338085
Devi, S.R., Arulmozhivarman, P., Venkatesh, C., et al.: Performance comparison of artificial neural network models for daily rainfall prediction. Int. J. Autom. Comput. 13, 417–427 (2016). https://doi.org/10.1007/s11633-016-0986-2
Eini, M., Kaboli, H.S., Rashidian, M., Hedayat, H.: Hazard and vulnerability in urban flood risk map**: machine learning techniques and considering the role of urban districts. Int. J. Disast. Risk Reduct. 50, 101687 (2020). https://doi.org/10.1016/j.ijdrr.2020.101687
Esfe, M.H., Toghraie, D.: Cascade forward artificial neural network to estimate the thermal conductivity of functionalized graphene-water nanofluids. Case Stud. Thermal Eng. 26, 101194 (2021). https://doi.org/10.1016/j.csite.2021.101194
Furquim, G., Pessin, G., Faiçal, B.S., et al.: Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory. Neural Comput. Appl. 27, 1129–1141 (2016). https://doi.org/10.1007/s00521-015-1930-z
Hayder, G., Solihin, M.I., Mustafa, H.M.: Modelling of river flow using particle swarm optimized cascade-forward neural networks: a case study of Kelantan River in Malaysia. Appl. Sci. 10(23), 8670 (December 4, 2020). https://doi.org/10.3390/app10238670
Jia, W., Zhao, D., Zheng, Y., et al.: A novel optimized GA–Elman neural network algorithm. Neural Comput. Appl. 31, 449–459 (2019). https://doi.org/10.1007/s00521-017-3076-
Karmakar, S., Simonovic, S.P., Peck, A., Black, J.: An information system for risk-vulnerability assessment to flood. J. Geogr. Inf. Syst. 2(03), 129 (2010). https://doi.org/10.4236/jgis.2010.23020
Keong, K.C., Mustafa, M., Mohammad, A.J., Sulaiman, M.H., Abdullah, N.R.H.: Artificial neural network flood prediction for Sungai ISAP residence. In: 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 236–241 (2016). https://doi.org/10.1109/I2CACIS.2016.7885321
Larestani, A., Mousavi, S.P., Hadavimoghaddam, F., Hemmati-Sarapardeh, A.: Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: gradient boosting decision tree and cascade-forward back-propagation network. J. Petrol. Sci. Eng. 208, 109315 (2022). https://doi.org/10.1016/j.petrol.2021.109315
Li, C., et al.: Runoff prediction method based on adaptive elman neural network. Water 11(6), 1113 (May 28, 2019). https://doi.org/10.3390/w11061113
Liu, H., Mi, X.W., Li, Y.F.: Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short-term memory neural network, and Elman neural network. Energy Convers. Manag. 156, 498–514 (2018). https://doi.org/10.1016/j.enconman.2017.11.053
Machado de Freitas, C., Elisa, F.X.: Floods and public health: a review of the recent scientific literature on the causes, consequences and responses to prevention and mitigation. Ciência Saúde Coletiva 17(6), 1601–1616 (06, 2012). https://doi.org/10.1590/S1413-81232012000600023. https://www.proquest.com/scholarly-journals/floods-public-health-review-recent-scientific/docview/1679223736/se-2
Nami, F., Deyhimi, F.: Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by an artificial neural network. J. Chem. Thermodyn. 43(1), 22–27 (2011). https://doi.org/10.1016/j.enconman.2017.11.053
Naumann, G., et al.: Global changes in drought conditions under different levels of warming. Geophys. Res. Lett. 45(7), 3285–3296 (2018). https://doi.org/10.1002/2017GL076521
Nevo, S., et al.: Flood forecasting with machine learning models in an operational framework. Hydrol. Earth Syst. Sci. 26(15), 4013–4032 (2022). https://doi.org/10.5194/hess-26-4013-2022
Ren, G., Cao, Y., Wen, S., Huang, T., Zeng, Z.: A modified Elman neural network with a new learning rate scheme. Neurocomputing 286, 11–18 (2018). https://doi.org/10.1016/j.neucom.2018.01.046
TerraClimate - Climatology Lab. (n.d.). Climatology Lab. www.climatologylab.org. Accessed 28 Aug 2022 https://www.climatologylab.org/terraclimate.html#:~:text=What%20is%20terraclimate%3F,resolution%20and%20time%2Dvarying%20data
United Nations Human Settlements Programme, pp. 14–15, 23 (2014). Archived from the original on 17 Oct 2017. Accessed 16 Oct 2017
VOH. Increase in Natural Disasters on a Global Scale by Ten Times. The vision of Humanity (2020, October 27). www.visionofhumanity.org. https://www.visionofhumanity.org/global-number-of-natural-disasters-increases-ten-times/#:~:text=Flooding%20is%20the%20most%20common,42%20per%20cent%20were%20floods
Wang, J., Zhang, W., Li, Y., Wang, J., Dang, Z.: Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl. Soft Comput. 23, 452–459 (2014). https://doi.org/10.1016/j.asoc.2014.06.027
Warsito, B., Rukun S., Yasin, H.: Cascade forward neural network for time series prediction. J. Phys. Conf. Ser. 1025(1), 012097 (2018). https://doi.org/10.1088/1742-6596/1025/1/012097. IOP Publishing
Zhang, X., Shen, B., Huang, L.: Grey self-memory model based on BP neural network for annual runoff prediction. J. Hydroelectr. Eng. 1, 68–71 (2009). https://doi.org/10.12677/jwrr.2012.13010
Yan, X., Xu, K., Feng, W., et al.: A rapid prediction model of urban flood inundation in a high-risk area coupling machine learning and numerical simulation approaches. Int. J. Disaster Risk. Sci. 12, 903–918 (2021). https://doi.org/10.1007/s13753-021-00384-0
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Bility, A.A., Aslanova, F., Elkiran, G. (2024). The Potential of Machine Learning for Tackling Flood Disaster in Monrovia. In: Aliev, R.A., et al. 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022). WCIS 2022. Lecture Notes in Networks and Systems, vol 912. Springer, Cham. https://doi.org/10.1007/978-3-031-53488-1_7
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