Road impact assessment modelling on plants diversity in national parks using regression analysis in comparison with artificial intelligence

Integr Envir Assess & Manag

2020

Self Cite

The effects of livestock and tourism on vegetation include loss of biodiversity and in some cases species extinction. To evaluate these stressor-effect relationships and provide a tool for managing them in Iran's Lar National Park, we developed a multilayer perceptron (MLP) artificial neural network model to predict vegetation diversity related to human activities. Recreation and restricted zones were selected as sampling areas with maximum and minimum human impacts. Vegetation diversity was measured as the number of species in 210 sample plots. Twelve landform and soil variables were also recorded and used in model development. Sensitivity analyses identified human intensity class and soil moisture as the most significant inputs influencing the MLP. The MLP was strong with R 2 values in training (0.91), validation (0.83), and test data sets (0.88). A graphical user interface was designed to make the MLP model accessible within an environmental decision support system tool for national park managers, thus enabling them to predict effects and develop proactive plans for managing human activities that influence vegetation diversity.

Section: Introductionmentioning

confidence: 99%

Human Activities Impact Prediction in Vegetation Diversity of Lar National Park in Iran Using Artificial Neural Network Model

Jahani¹,

Integr Envir Assess & Manag

2020

Self Cite

“…Such as other prediction models in forests 43 – 46 , 50 , 51 , TFM mlp was developed for forest managers to assess quickly the impact of cutting trees or thinning stands on tree failure risk in forest. Such as an early warning system 53 , 54 , TFM mlp would allow simulations of management approaches on the windstorm damage risk.…”

Section: Discussionmentioning

confidence: 99%

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

Jahani¹,

2021

Sci Rep

Self Cite

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.

“…In this paper, input variables include landform, phenological stages, and soil characteristics, and the output variable is the content of hyperforin. The MLP model by weighting variables and summarizing them produced the most accurate output in previous study by Shams et al (2020Shams et al ( , 2021 and Pourmohammad et al (2020). At first, 60% of samples put in use in the training process.…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 91%

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Jahani²,

Jahani

2021

Plant Direct

Self Cite

Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model (R2 = .9) is the most suitable and precise model compared with RBF (R2 = .81) and SVM (R2 = .74) in predicting hyperforin in H. perforatum based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.