| Type: | Package |
| Title: | Artificial Intelligence Based Machine Learning Algorithms for Height Diameter Relationships of Conifer Trees |
| Version: | 0.1.0 |
| Author: | Dr. M. Iqbal Jeelani [aut, cre], Dr. Fehim Jeelani [aut], Dr. Shakeel Ahmad Mir [aut], Dr. Syed Naseem Geelani [aut], Dr. Mushtaq Ahmad Lone [aut], Dr. Asif Ali [aut], Dr. Tahir Mushtaq [aut], Dr. Amir Bhat [aut], Dr. Md Yeasin [aut] |
| Maintainer: | Dr. M. Iqbal Jeelani <jeelani.miqbal@gmail.com> |
| Description: | Estimating height of forest plant is one of the key challenges of recent times. This package will help to fit and validate AI (Artificial Intelligence) based machine learning algorithms for estimation of height of conifer trees based on diameter at breast height as explanatory variable using algorithm of Paul et al. (2022) <doi:10.1371/journal.pone.0270553>.. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | stats, randomForest, e1071, xgboost, ggplot2, reshape2, rpart |
| RoxygenNote: | 7.2.1 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2023-09-11 12:42:17 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2023-09-12 06:12:44 UTC |
Artificial Intelligence Based Machine Learning Algorithms for Height Diameter Relationships of Conifer Trees
Description
Artificial Intelligence Based Machine Learning Algorithms for Height Diameter Relationships of Conifer Trees
Usage
ImHD(data, splitratio = 0.7)
Arguments
data |
Datasets |
splitratio |
Train-Test split ratio |
Value
Prediction: Prediction of all ML models
Accuracy: Accuracy metrics
References
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. <doi:10.1002/9781118032985>
Examples
library("ImHD")
data <- system.file("extdata", "data_test.csv", package = "ImHD")
data_test <- read.csv(data)
Model<-ImHD(data =data_test)