Document Type : Original Research Paper


Department of Electronic and Electrical Engineerin, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.


This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, while other portions were used to check and test the generalization ability of the ANFIS model. Water level predictions were made for 24 hours, 48 hours and 72 hours, in which training, checking and testing of the model were performed for each of the prediction periods. The model results from the training, checking and testing data groups show that 48 hours prediction has the least Root Mean Square Error (RMSE) of 0.05426, 0.06298 and 0.05355 for training, checking and testing data groups respectively, showing that the prediction is most accurate for 48 hours.


Main Subjects

[1] Alvisi, S., Mascellani, G., Franchini, M., and Bardossy, A., 2006. Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences, 10, pp.1–17.
[2] World Meteorological Organization, 2011. Manual on flood forecasting and warning, 1072.
[3] Jyh-Shing, R. J., Chuen-Tsai, S., Eiji, M., 1997. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice Hall Inc.
[4] Eti, F. I., 2018. Simulating adaptive neuro fuzzy inference system (ANFIS) training using student grade data. International Journal of Innovative Scientific & Engineering Technologies Research, 6(2), pp. 59-65.
[5] Vashisht, V., Lal, M., Sureshchandar, G. S., 2016. Defect prediction framework using adaptive neuro-fuzzy inference system (ANFIS) for software enhancement projects. British Journal of Mathematics & Computer Science, 19(2), pp. 1-12.
[6] New Jersey, 1997. Chang, F., Chang, Y. T., 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29, pp.1–10.
[7] Altug, S., Chow, M. Y., Trussell, H. J., 1999. Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. IEEE Transactions on Industrial Electronics, 46,(6), pp. 1069–1079.
[8] Sharad, T., Richa, B., Gadandeep, K., 2018. Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India). Advances In Civil Engineering, 2018, pp. 1-10.
[9] William, G. C., 2000. The American Heritage Dictionary of the English Language, 4th ed. Houghton Mifflin Co.
[10] Nelson, S. A., 2007. Coastal zones. Natural Disasters, EENS 2040, Tulane University, LA.
[11] Darwin, R. F., Richard, S. J., 2001. Estimate of the economic effect of sea level rise. Environmental and Resource Economics, 19(2), pp.113-129.
[12] Titus, J. G., Anderson, K. E., Cahoon, D. R., Gesch, D. B., Gill, S. K., et al, 2009. A report by the U.S. climate change science program and the subcommittee on global change research. U. S. Environmental Protection Agency, Washington, DC, USA.
[13] Karl, T. R., Melillo, J. M., Peterson, T. C., 2009. Global climate change impacts in the United States. United States Global Change Research Program. Cambridge University Press.
[14] Alaurah, M., Marco, M., 2016. Coastal water table mapping: Incorporating groundwater data into flood inundation forecasts. M.Sc Thesis, Nicholas School of the Environment, Duke University.