IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Neural Network for Modeling Multicategorical Parcel Use Change

A Neural Network for Modeling Multicategorical Parcel Use Change
View Sample PDF
Author(s): Kang Shou Lu (Towson University, USA), John Morgan (Towson University, USA) and Jeffery Allen (Clemson University, USA)
Copyright: 2013
Pages: 12
Source title: Emerging Methods and Multidisciplinary Applications in Geospatial Research
Source Author(s)/Editor(s): Donald P. Albert (Sam Houston State University, USA) and G. Rebecca Dobbs (University of North Carolina - Chapel Hill, USA)
DOI: 10.4018/978-1-4666-1951-7.ch008

Purchase

View A Neural Network for Modeling Multicategorical Parcel Use Change on the publisher's website for pricing and purchasing information.

Abstract

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

Related Content

Salwa Saidi, Anis Ghattassi, Samar Zaggouri, Ahmed Ezzine. © 2021. 19 pages.
Mehmet Sevkli, Abdullah S. Karaman, Yusuf Ziya Unal, Muheeb Babajide Kotun. © 2021. 29 pages.
Soumaya Elhosni, Sami Faiz. © 2021. 13 pages.
Symphorien Monsia, Sami Faiz. © 2021. 20 pages.
Sana Rekik. © 2021. 9 pages.
Oumayma Bounouh, Houcine Essid, Imed Riadh Farah. © 2021. 14 pages.
Mustapha Mimouni, Nabil Ben Khatra, Amjed Hadj Tayeb, Sami Faiz. © 2021. 18 pages.
Body Bottom