ENIVRONMENT ASIA

Volume 13, No. 01, Month JANUARY, Year 2020, Pages 41 - 52

Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction

Tika Olivia Bt Muslim, Ali Najah Ahmed and Marlinda Abdul Malek

Abstract Download PDF

Sea Level Rise (SLR) is one of the most difficult elements to predict in the hydrological cycle. 12% of the area of Peninsular Malaysia, where the western low plains of muddy sediment are home to 2.5 million people, is vulnerable to flooding. In this study, two Artificial Intelligence (AI) techniques were used to predict SLR, namely, the Multi-Layer Perceptron Neural Network (MLP-NN) and Random Forest Regression (RFR) techniques. This studied, two cases were presented. The first case (Case 1) was to establish the prediction model for SLR by a monthly data set, while the second case (Case 2) was by means of a cyclical data set. From sensitivity analysis result, it was found that the most effective meteorological input parameters were rainfall (mm) and wind direction (degree). The performance of the models was evaluated according to three statistical indices in terms of the correlation coefficient (R), root mean square error (RMSE) and scatter index (SI). A comparison of the results of the MLP-NN and RFR showed that the MLP-NN performed better than the latter as the R obtained in Case 2 of the MLP-NN was 0.733 with 65.652 and 2.735 for RMSE and SI respectively. Meanwhile, accuracy improvement percentage (%AI) was 8%.

Keywords

Sea level; ANN; Multi-layer Perceptron Neural Network; Random Forest Regression

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