Immune Particle Swarm Optimization for Support Vector Regression on Forest Fire Prediction

Wang, Yan; Wang, Juexin; Du, Wei; Wang, Chuncai; Zhou, Chunguang; Huang, Lan

doi:10.1007/978-3-642-01510-6_44

Cited by 6 publications

(1 citation statement)

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“…The prerequisite for SVR to achieve better results, is the appropriate determination of the parameters that play key roles in achieving higher accuracy and better performance [74]. The specified grid search using v-fold cross-validation [52] is the most commonly used method to identify suitable parameters, i.e., epsilon (ε) and capacity (C) with fixed gamma that would produce high-accuracy results.…”

Section: Implementation Of Machine Learning Methodsmentioning

confidence: 99%

Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

Abdollahnejad

Panagiotidis

Shataee

et al. 2017

Forests

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Abstract:Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF) frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM). Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF), support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate) with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

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Section: Implementation Of Machine Learning Methodsmentioning

confidence: 99%