| Title: | Neural Networks for Predicting Volume of Forest Trees |
|---|---|
| Description: | Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>. |
| Authors: | M. Iqbal Jeelani [aut, cre], Fehim Jeelani [aut], Shakeel Ahmad Mir [aut], Syed Naseem Geelani [aut], Mushtaq Ahmad Lone [aut], Nazir A. Pala [aut], Faizan Danish [aut], Afshan Tabassum [aut], Khalid Ul Islam [aut], Imran Rashid [aut], Md Yeasin [aut] |
| Maintainer: | M. Iqbal Jeelani <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-03-06 03:17:04 UTC |
| Source: | https://github.com/cran/ImNN |
Neural Networks for Predicting Volume of Forest Trees
ImNN(data, hidden_neurons_range)ImNN(data, hidden_neurons_range)
data |
Datasets |
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Number of hidden neurons in neural network's two layers (layer 1 and layer 2) |
results: Results
Jeelani, M.I., Tabassum, A., Rather, K and Gul,M.2023. Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169–178
Tabassum, A., Jeelani, M.I., Sharma,M., Rather, K R ., Rashid, I and Gul,M.2022. Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine . Agricultural Science Digest - A Research Journal. DOI:10.18805/ag.D-5555
library("ImNN") data <- system.file("extdata", "data_test.csv", package = "ImNN") data_test <- read.csv(data) Model<-ImNN(data =data_test,hidden_neurons_range=2)library("ImNN") data <- system.file("extdata", "data_test.csv", package = "ImNN") data_test <- read.csv(data) Model<-ImNN(data =data_test,hidden_neurons_range=2)