Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

Shafizadeh‐Moghadam, Hossein; Hagenauer, Julian; Farajzadeh, Manuchehr; Helbich, Marco

doi:10.1080/13658816.2014.993989

Cited by 35 publications

(30 citation statements)

References 44 publications

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“…The network repeatedly receives the numbers of input/output pairs and the error is propagated from the output back to the input layer. The learning rate and update rule renew the weights of the backward paths [72,73]. In addition, the default processing unit, training and learning rate cannot uniquely specify the ANN.…”

Section: Methods For Land-use/land-cover (Lulc) Modellingmentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

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Section: Methods For Land-use/land-cover (Lulc) Modellingmentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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“…Moreover, the nonlinear relationships between LULC change and its driving factors, such as population growth, policies and socio-economic variables, can be addressed smoothly by the MLP modeling framework. The MLP has been used in some studies to support binary and multiclass LULC change simulations [50,[53][54][55]. The MLP model has the proven advantage of dealing with non-linear relationships without requiring the transformation of variables [56].…”

Section: Mlp_markov Chain Model (Mlp_mc)mentioning

confidence: 99%

Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut

Wang

Zhang

Allen

et al. 2016

Land

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Land use and land cover (LULC) patterns play an important role in the establishment and spread of invasive plants. Understanding LULC changes is useful for early detection and management of land-use change to reduce the spread of invasive species. The primary objective of this study is to analyze and predict LULC changes in Connecticut. LULC maps for 1996, 2001 and 2006 were selected to analyze past land cover changes, and then potential LULC distribution in 2018 was predicted using the Multi-Layer Perceptron Markov Chain (MLP_MC) model. This study shows that the total area of forest has been decreasing, mainly caused by urban development and other human activity in Connecticut. The model predicts that the study area will lose 5535 ha of deciduous forest and gain 3502 ha of built-up area from 2006 to 2018. Moreover, forests near built-up areas and agriculture lands appear to be more vulnerable to conversion. Changes in LULC may result in subtle spatial shifts in invasion risk by an abundant invasive shrub, Japanese barberry (Berberis thunbergii). The gain of developed areas at the landscape scale was most closely linked to increased future invasion risk. Our findings suggest that the forest conversion needs to be controlled and well managed to help mitigate future invasion risk.

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“…Taravat et al [13] introduced and evaluated a multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI images. The radial basis function network and multilayer perceptron networks were investigated in article [14] for modeling urban change. Fan et al [15] set up a multilayer perceptron neural network prediction model based on phase reconstruction, which is for carbon price to characterize its strong nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

Silin

2016

J Embedded Systems

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For improving the system performance of mobile Internet, how to provide the Quality of Experience (QoE) guarantee is an important factor. First, based on artificial neural network and adaptive cross-layer perceptron, we studied the cloudassisted QoE guarantee mechanism. Then, according to the power, we divided the distance and perceptron layers of mobile Internet and cloud into three levels. We showed the state information definition of the mobile node on the basis of the adaptive adjustment perceptron layers. Thirdly, the perceptron network topology would be updated according to the customer service, which would be updated based on the perceptron learning rule for improving the training practice efficiency. The above scheme would guarantee the QoE effectively. The experimental results show that the proposed QoE guarantee mechanism has obvious advantages in terms of throughput, efficiency, and reliability.

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Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

Cited by 35 publications

References 44 publications

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut

Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

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