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

Abstract

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.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

“…Artificial neural networks (ANNs) are mathematical models that can be used for complex and non-linear processes 16 , 17 . ANNs can simulate the behavior of the human brain 18 , 19 .…”

“…There are increasing levels of NO 2 pollution in many countries, especially in metropolises such as Tehran. Therefore forecast, controlling and counteracting NO 2 is a vital issue in urban management 4 .Artificial neural networks (ANNs) are mathematical models that can be used for complex and non-linear processes 16,17 . ANNs can simulate the behavior of the human brain 18,19 .…”

Changes in the essential oil content and composition of Salvia limbata C.A. Mey at different growth stages and altitudes

“…In a published study authors observed the signi cant impact of different altitudes and phenological stages on the essential oil yield [22,23]. It was revealed that plant performance is strongly in uenced by various factors such as altitude, climate, soil, developmental stages, extraction and analysis methods, genetic factors, abiotic stresses, and slope and modeling techniques can predict these factors in other areas [24,25,26,27,28]. The results of the current study were compatible with other studies that found the highest content of essential oil of Origanum majorana in the vegetative stage, so they proposed the vegetative stage as the best stage to harvest Origanum majorana [29,30,31,32,33,34,35,36].Similar results were also found byon the essential oil content of Nepeta kotschyr [37].…”

“…It was revealed that plant performance is strongly influenced by various factors such as altitude, climate, soil, developmental stages, extraction and analysis methods, genetic factors, abiotic stresses, and slope. Modeling techniques were used to predict these factors (Jahani Goshtasb et al, 2020;Jahani & Rayegani, 2020;, 2021Saffariha et al, 2020). Because climate is directly related to changes in altitude, when we considered altitude, climate, and temperature have been examined then.…”

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

“…In fact, ANNs are capable of learning from real samples of a problem using transfer functions between neurons and specific algorithms without any prior knowledge. 16,17 ANNs have been proposed as a reasoning method due to high-performance characteristics and flexibility in modeling complex relationships than the traditional statistical methods used for the extraction of prediction equations in clinical decision making. This is due to the fact that altering the number of layers and hidden units, applying different algorithms, changing the training cycles or modifying the weight trying to find the best solution is easy in a neural network.…”