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Showing 5 results for Artificial Neural Network

Ali Jahani,
Volume 3, Issue 4 (1-2017)
Abstract

Trees in urban areas have survived in a wide variety of conditions and constrains, whether developing in natural or manmade habitats. Due to environmental constrains and stresses, urban trees rarely achieve their biological potentials. Indeed, some of trees, in small groups, could excel in terms of age, biomass structure and dimensions in urban areas. In definition, tree hazard includes entirely dead or dying trees, dead parts of harmed live trees, or extremely unstable or unsteady live trees, which could be in result of structural defects and disorders or other factors that have the high risk to threaten the safety of people or property in the event of a failure especially in urban green spaces. Although the pruning or other rehabilitation and mitigation program of trees is known as the one of the principal domains of green space management, it is still includes shortcomings in terms of models and methodologies to classify or prioritize hazardous trees which need to be treated timely. The main objectives of this study were to: (1) model old Sycamore failure hazard in urban green spaces to elucidate the general and defects tree factors affecting on failure hazard; (2) prioritize the impacts of model inputs (general and defects tree factors) on tree failure hazard using model sensitivity analysis and (3) determining the trend model output changes in respond to model variables changes.

The following types of data (target trees characteristics) were solicited for each target tree: (1) General features: Tree Height (TH), trunk Diameter at Breast Height (DBH), Butt Diameter (BD) at ground surface and Vertical Length of Crown (VLC) were calculated from measured girth. Crown Spread (CS) was measured as the average of two diameters of projected drip line of the tree canopy.

(2) Tree defects: Detailed evaluation of individual trees was made according to 6 key physical defects, namely Internal Decay (ID) in percent, Length of Cracks (LC) in m, Crown Defoliation (CD) in percent, and Degree of Leaning (DL).

(3) Sycamore failure hazard classification: Sycamore Failure Hazard Risk (SFHR) classification was the probability that an entire tree, or part of it, will break and fall within the first or second year after study. Considering results of tree regular monitoring after two years, the following classes of tree failure hazard were determined. 1. Extremely Hazardous: Tree failure in the first year. 2. Semi-Hazardous: Tree failure in the second year.

ANN has been recently developed for data mining, pattern recognition, quality control, and has gained wide popularity in modeling of many processes in environmental sciences and engineering. ANN learns by examples and it can combine a large number of variables. In this study, an ANN is considered as a computer program capable of learning from samples, without requiring a prior knowledge of the relationships between parameters. To objectively evaluate the performance of the network, two different statistical indicators were used. These indicators are Mean-Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2).

In this study, the year of Sycamore failure in urban ecosystems is evaluated using tree variables and artificial neural network to determine the most effective tree variables in SFHR in urban green space. Various MLFNs were designed and trained as one and two layers to find an optimal model prediction for the SFHR and variables. Training procedure of the networks was as follows: different hidden layer neurons and arrangements were adapted to select the best production results. Altogether, many configurations with different number of hidden layers (varied between one and two), different number of neurons for each of the hidden layers, and different inter-unit connection mechanisms were designed and tested.

In this research, 200 trees were totally selected, then general and defects tree variables were recorded in urban green space. Considering the aim of study, which is discovering the relation between general and defects tree variables with SFHR class for modeling, the year of tree failure, was recorded.  

In the structure of artificial neural network, general and defects tree variables were tagged as inputs of artificial neural network and SFHR class was tagged as output layer. Considering trained networks (the structure of optimum artificial neural network has been summarized in Table1), Multilayer Perceptron network with one hidden layer and 4 neurons in layer created the best function of topology optimization (Table2) with higher coefficient of determination which equals 0.87 for class 1 and 0.9 for class 2. Sensitivity analysis respectively prioritizes Crown Spread (CS), Vertical Length of Crown (VLC), Degree of Leaning (DL) and Butt Diameter (BD), which effect on SFHR in class1 (Fig1) and class 2 (Fig2).

The determined procedure of SFHR changes with CS changes in the region declares SFHR increase nonlinearly with an increase in CS. The determined procedure of SFHR changes with VLC changes o declares that SFHR increase nonlinearly with an increase in VLC of tree. The determined procedure of SFHR changes with DL changes in the region declares SFHR increase nonlinearly with an increase in DL. The determined procedure of SFHR changes with BD changes o declares that SFHR increase nonlinearly with an increase in BD of tree.

Nowadays, artificial neural network modeling in natural environments has been applied successfully in many researches such as water resources management, forest sciences and environment assessment. The results of research declared that designed neural network shows high capability in SFHR modeling which is applicable in green space management of studied area. Sensitivity analysis identified the most effective variables which are influencing SFHR. So, to identify hazardous trees in study area, we should pay attention to the CS of Sycamore trees as the variable with high priority in determination of SFHR. We believe that, in hazardous trees management in urban green spaces, we should pay attention to some modifiable factors of tree, which are CS and VLC, by timely tree pruning. We suggest urban green space manager to run SFHR model, for tree stability assessment, before decision making on hazardous trees.


Ali Jahani,
Volume 6, Issue 2 (9-2019)
Abstract

Risks assessment of forest project implementation in spatial density changes of forest under canopy vegetation using artificial neural network modeling approach
 
Nowadays, environmental risk assessment has been defined as one of the effective in environmental planning and policy making. Considering the position and structure of vegetation on the forest floor, the main role of forest under canopy vegetation cover can be noted in attracting and preventing runoff in the forest floor and reducing subsequent environmental risks. The purpose of this article is forest under canopy vegetation density changes modeling considering forest ecosystem structure and forest management activities as an environmental risk. The main objectives of this study were to: (1) model forest under canopy vegetation density in forest ecosystem to elucidate the ecological and management factors affecting on under canopy vegetation density; (2) prioritize the impacts of model inputs (ecological and management factors) on under canopy vegetation density using model sensitivity analysis and (3) determining the trend model output changes in respond to model variables changes.
In this study, Land Management Units (LMUs) were formed in the region considering ecological characteristics of land. LMUs were mapped out based on Ian McHarg’s overlay technique by ARC GIS 9.3 software. Ecological factor classes of an LMU differ from ecological factor classes of adjacent LMUs (at least in one ecological factor class). The following types of data were solicited for each LMU:
(1) Ecological variables: Altitude or elevation (El), Slope (Sl), Aspect (As), soil depth (SD), Soil Drainage (SDr),Soil Erosion (SE), Precitipation (Pr), Temprature (Te), trees Diameter at Breast Height (DBH), Canopy Cover (CC), and forest Regeneration Cover (RC).
(2) Management variables: Cattle Density (CD), Animal husbandry Dsitance (AD), Road Dsitance (RD), Trail Dsitance (TD), logs Depot Dsitance (DD), Soil Compaction (SC), Torist impacts (To), Skidding impacts (Sk), Logging impacts (Lo), Harvested trees volume (Ha), artificial Regeneration (Re) and Seed Planting (SP).
(3) Forest under canopy vegetation density: The percentage of under canopy vegetation density in each LMU was estimated by systematic random sampling method. In each LMU, a one square meter sample was taken. The average percentage of under canopy vegetation density in sample units of each LMU was calculated and used in the modeling process.
ANN learns by examples and it can combine a large number of variables. In this study, an ANN is considered as a computer program capable of learning from samples, without requiring a prior knowledge of the relationships between parameters. To objectively evaluate the performance of the network, four different statistical indicators were used. These indicators are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2).
Various MLFNs were designed and trained as one and two layers to find an optimal model prediction for the under canopy vegetation density and variables. Training procedure of the networks was as follows: different hidden layer neurons and arrangements were adapted to select the best production results. Altogether, many configurations with different number of hidden layers (varied between one and two), different number of neurons for each of the hidden layers, and different inter-unit connection mechanisms were designed and tested.
In this research, 129 LMUs were totally selected, then ecological and management variables were recorded in them. In the structure of artificial neural network, ecological and management variables were tagged as inputs of artificial neural network and the percentage of under canopy vegetation density was tagged as output layer. Considering trained networks (the structure of optimum artificial neural network has been summarized in Table1), Multilayer Perceptron network with one hidden layer and 4 neurons in each hidden layer created the best function of topology optimization with higher coefficient of determination of test data (which equals 0.857) and the lowest MSE and MAE (which are 0.866 and 0.736 respectively). Considering the results of sensitivity analysis, ecological and management variables like the forest canopy density, cattle density in forest, soil erosion and soil compaction respectively show the highest impact on forest under canopy vegetation density changes (Fig1).
 
Table1. The structure of optimum artificial neural network in forest under canopy vegetation density

Output Layer First Hidden Layer Network features
Linear Hyperbolic tangent Transmission Layer
Gradient descent Gradient descent Optimization Algorithm
0.7 0.7 Momentum
1 4 Number of Neurons
-0.9 up to 0.9 -0.9 up to 0.9 Normalization
 
Table2. The structure of optimum artificial neural network in test data
MSE MAE RMSE R2 Data The structure of network( the number of neurons)-epoch
0.716 0.678 0.846 0.931 Trainning Tanh(4)-160
0.793 0.703 0.891 0.894 Validation
0.866 0.736 0.931 0.857 Test

 

 
Fig1. The results of sensitivity analysis of artificial neural network model
 
Nowadays, artificial neural network modeling in natural environments has been applied successfully in many researches such as water resources management, forest sciences and environment assessment. The results of research declared that designed neural network shows high capability in forest under canopy vegetation density modeling which is applicable in forest management of studied area. Sensitivity analysis identified the most effective variables which are influencing under canopy vegetation density.
So, to identify hazardous LMUs in study area, we should pay attention to the canopy density of LMUs as the variable with high priority in determination of under canopy vegetation density. We believe that, in hazardous LMUs in forests, we should pay attention to some modifiable factors of LMU, which is cattle density in forest, by timely plan for livestock elimination. The forest under canopy vegetation density assessment model, in forest projects impact assessment, could be a solution in decision making about forest plan structure and implementation of similar projects in similar locations. 
 
Keywords: Forest plan, Environmental impact assessment, Multilayer perceptron, under canopy vegetation, artificial neural network
 


Zahra Mosaffaei, Ali Jahani, Mohammad Ali Zare Chahouki, Hamid Goshtasb Meygoni, Vahid Etemad,
Volume 8, Issue 3 (12-2021)
Abstract

Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park
 
Zahra Mosaffaei1, Ali Jahani2*, 3MohammadAli ZareChahouki, 4Hamid GoshtasbMeygoni, 5Vahid Etemad
 
1 Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj
*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.
3 Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj
4 Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj
5 Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj
 
 
Abstract
Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
 
Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation
 
Leila Ahadi, Hossein Asakereh, Younes Khosravi,
Volume 10, Issue 2 (9-2023)
Abstract

Simulation of Zanjan temperature trends based on climate scenarios and artificial neural network method

Abstract
Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.

Research Methods
The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.

results
According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.

Keywords: climate scenarios, simulation, extreme temperatures, artificial neural network, Zanjan



 
Hayedeh Ara, Zahra Gohari, Hadi Memarian,
Volume 10, Issue 3 (9-2023)
Abstract

Introduction
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of  the most important factors (David and Nicholas, 1994). Sand areas are one of the desert  landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding  and  interaction with the environment and ultimately its sustainable management  and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.

Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.

 


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