TY - JOUR
ID - 13470
TI - Coastal Water Level Prediction Model Using Adaptive Neuro-fuzzy Inference System
JO - Journal of Advances in Computer Engineering and Technology
JA - JACET
LA - en
SN - 2423-4192
AU - ADIGUN, OLATUNJI HEZEKIAH
AU - OYEDELE, OLUSOLA JOEL
AD - Department of Electronic and Electrical Engineerin, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.
Y1 - 2019
PY - 2019
VL - 5
IS - 1
SP - 11
EP - 18
KW - Coastal Area
KW - Fuzzy Logic
KW - Neural Network
KW - RMSE
DO -
N2 - 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.
UR - https://jacet.srbiau.ac.ir/article_13470.html
L1 - https://jacet.srbiau.ac.ir/article_13470_bd902e3d0af08ca07aab1795c9cc3db4.pdf
ER -