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Human Activities Impact Prediction in Vegetation Diversity of Lar National Park in Iran Using Artificial Neural Network Model
Author(s) -
Jahani Ali,
Saffariha Maryam
Publication year - 2021
Publication title -
integrated environmental assessment and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 57
eISSN - 1551-3793
pISSN - 1551-3777
DOI - 10.1002/ieam.4349
Subject(s) - vegetation (pathology) , national park , biodiversity , recreation , environmental science , artificial neural network , environmental resource management , sampling (signal processing) , geography , ecology , computer science , machine learning , medicine , archaeology , filter (signal processing) , pathology , computer vision , biology
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. Integr Environ Assess Manag 2021;17:42–52. © 2020 SETAC