Version 1
: Received: 8 March 2021 / Approved: 9 March 2021 / Online: 9 March 2021 (09:43:54 CET)
How to cite:
Ramdani, F.; Setiawan, B.; Rusydi, A.; Furqon, M. An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia. Preprints2021, 2021030247. https://doi.org/10.20944/preprints202103.0247.v1
Ramdani, F.; Setiawan, B.; Rusydi, A.; Furqon, M. An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia. Preprints 2021, 2021030247. https://doi.org/10.20944/preprints202103.0247.v1
Ramdani, F.; Setiawan, B.; Rusydi, A.; Furqon, M. An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia. Preprints2021, 2021030247. https://doi.org/10.20944/preprints202103.0247.v1
APA Style
Ramdani, F., Setiawan, B., Rusydi, A., & Furqon, M. (2021). An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia. Preprints. https://doi.org/10.20944/preprints202103.0247.v1
Chicago/Turabian Style
Ramdani, F., Alfi Rusydi and Muhammad Furqon. 2021 "An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia" Preprints. https://doi.org/10.20944/preprints202103.0247.v1
Abstract
Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.
Keywords
artificial neural network; land use land cover; Indonesia; land use prediction
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
