Artificial Neural Networks

Broese, Einar; Löffler, Hans Ulrich

doi:10.1002/3527603786.ch7

Cited by 2 publications

(1 citation statement)

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“…The MLP method is the most common type of neural network [58], and it is often called a "back-propagation" network because of its training rules. The MLP consists of an input layer, one or more hidden layers and an output layer [59,60] as shown in Figure 4. Multi-layer perceptrons are often fully connected, i.e., every neuron in each layer is connected to other neurons in neighboring layers [61].…”

Section: Mlp-mcmentioning

confidence: 99%

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Öztürk

2015

Remote Sensing

148

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Abstract:In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area.

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Section: Mlp-mcmentioning

confidence: 99%