Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

Abstract In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting an...

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Main Authors: Ali Jahani, Maryam Saffariha
Format: Article
Language:English
Published: Nature Publishing Group 2021-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-020-80426-7
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spelling doaj-663fad4bce1c46139d4aaa11a4c217c02021-01-17T12:42:13ZengNature Publishing GroupScientific Reports2045-23222021-01-0111111310.1038/s41598-020-80426-7Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniquesAli Jahani0Maryam Saffariha1Research Center of Environment and Sustainable Development, College of EnvironmentDepartment of Rangeland Management, College of Natural Resources, University of TehranAbstract In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.https://doi.org/10.1038/s41598-020-80426-7
collection DOAJ
language English
format Article
sources DOAJ
author Ali Jahani
Maryam Saffariha
spellingShingle Ali Jahani
Maryam Saffariha
Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
Scientific Reports
author_facet Ali Jahani
Maryam Saffariha
author_sort Ali Jahani
title Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
title_short Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
title_full Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
title_fullStr Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
title_full_unstemmed Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
title_sort modeling of trees failure under windstorm in harvested hyrcanian forests using machine learning techniques
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-01-01
description Abstract In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.
url https://doi.org/10.1038/s41598-020-80426-7
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