Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing

Wishnumurti Wicaksono; Sani M.  Isa

doi:10.5614/itbj.ict.res.appl.2022.16.1.4

Authors

Wishnumurti Wicaksono Computer Science Department, Binus Graduate Program, Bina Nusantara University, 11530, Indonesia
Sani M. Isa Computer Science Department, Binus Graduate Program, Bina Nusantara University, 11530, Indonesia

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2022.16.1.4

Keywords:

MNDWI, neural network, regression, remote sensing, Sidoarjo mudflow

Abstract

The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550.

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