An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

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.

Artificial Intelligence Techniques for Observation of Earth’s Changes

“…It may be combined with various decision techniques to form an outfit learning classifier, such as a self-assertive woods classifier. Observe [39] that the difference between TDC & RF is apparent in Table 4.…”

“…Rule-based methodology, data-driven methodologies, ensemble techniques, and reinforcement learning approaches are discussed. This file [39] contains all of the order's divisions and calculations.…”

“…It is commonly thought that highlight focuses of the same class are grouped in component space. The mean vector governed by this element focuses as the class's focal point, and the covariance network represents the scattering of encompassing focuses [39]. Every type has its own set of estimates for focus.…”

“…The decision boundaries in an image data space form an n-dimensional parallelepiped. A standard deviation threshold from the mean for each selected class determines the dimensions of a parallelepiped classifier in Figure 12 [39].…”

Classification of land use/land cover using artificial intelligence (ANN-RF)

“…There are seven to 10 layers in a deep learning network, each housing thousands of artificial neurons (Alqadhi et al, 2021), and the neural networks work on a feedforward basis. This form of an artificial neural network is the most frequent (Ramdani et al, 2021). This arrangement only passes through the "hidden" levels from the input to the output.…”