Deep neural network-based prediction of tsunami wave attenuation by mangrove forests - Adytia, Didit; Tarwidi, Dede; Saepudin, Deni; Husrin, Semeidi; Abdul Rahman, Mohd Kasim; Mohd Fakhizan, Romlie; Samsudin, Dafrizal (Elsevier B.V., 2024)
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost. • High-accuracy DNN models require hyperparameter optimization and neural network architecture selection. • The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %. • DNN can serve as an alternate predictive model to empirical formulas or classical numerical models.