Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan

Abstract This article proposes random forest algorithm (RFA), multi-layer perception (MLP) artificial neural network (ANN), and support vector machine (SVM) method for classifying the fused data of Sentinel-2, Landsat-8, and Airbus Vision-1 satellites for the years 2016 and 2023. The first variant of fusion is performed for Sentinel-2 and Landsat-8 data to sharpen it to 10 m spatial resolution, while in the second case, Sentinel-2 and Airbus Vision-1 data are fused together to achieve a spatial resolution of 3.48 m. MLP-ANN, SVM, and RFA methods are applied to the sharpened dataset for the years 2023 and 2016 having spatial resolutions of 3.48 and 10 m, respectively, and a detailed comparative analysis is performed. Google earth engine is utilized for ground data validation of the classified samples. An enhanced convergence time of 100 iterations was achieved using MLP-ANN for the classification of the dataset at 3.48 m spatial resolution, while the same method took 300 iterations with the dataset at 10 m spatial resolution to achieve a minimum limit Kappa hat score of 0.85. With 10 m spatial resolution, the MLP-ANN achieved an overall accuracy of 96.6% and a Kappa hat score of 0.94, while at 3.48 m spatial resolution, the aforementioned scores are enhanced to 98.5% and 0.97, respectively. Similarly, with 10 m spatial resolution, the RFA achieved an overall accuracy of 92.6% and a Kappa hat score of 0.88, while at 3.48 m spatial resolution, the abovementioned scores are enhanced to 96.5 and 0.95% respectively. In view of the forgoing, the MLP-ANN showed better performance as compared to the RFA method. Graphical abstract