Version 1
: Received: 7 March 2018 / Approved: 7 March 2018 / Online: 7 March 2018 (04:58:04 CET)
Version 2
: Received: 26 December 2018 / Approved: 27 December 2018 / Online: 27 December 2018 (11:42:03 CET)
How to cite:
Ahmadi, V. Using GIS and Artificial Neural Network for Deforestation Prediction. Preprints2018, 2018030048. https://doi.org/10.20944/preprints201803.0048.v2
Ahmadi, V. Using GIS and Artificial Neural Network for Deforestation Prediction. Preprints 2018, 2018030048. https://doi.org/10.20944/preprints201803.0048.v2
Ahmadi, V. Using GIS and Artificial Neural Network for Deforestation Prediction. Preprints2018, 2018030048. https://doi.org/10.20944/preprints201803.0048.v2
APA Style
Ahmadi, V. (2018). Using GIS and Artificial Neural Network for Deforestation Prediction. Preprints. https://doi.org/10.20944/preprints201803.0048.v2
Chicago/Turabian Style
Ahmadi, V. 2018 "Using GIS and Artificial Neural Network for Deforestation Prediction" Preprints. https://doi.org/10.20944/preprints201803.0048.v2
Abstract
Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.
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.
