نوع مقاله : مقاله کامل علمی پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، ایران،
2 استادیار گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد و آزمایشگاه مرکزی، دانشگاه شهرکرد، ایران،
3 استادیار گروه مرتع و آبخیرداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد و آزمایشگاه مرکزی، دانشگاه شهرکرد، ایران،
4 دانشیار گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Background and Objectives: Street trees in urban green space, despite all the benefits, any defect due to old tree age, loss of tree resistance because of the industrialization, population density and pollution of the big cities, as well as repeated droughts, can lead to the risk of personal injury or damage to property. Therefore, the importance of exploring and identifying hazardous trees has increased the in the large cities. For this propose, the estimation of the risk possibility of plane trees (Plantanus orientalis L.) in the green space of Abbasabad-Abad in Isfahan and their fall risk model prediction was done using Artificial Neural Network.
Material and Methods: Isfahan was studied, using data coming from a full survey method, and using quantitative tree body proportions and few risk factors (qualitative or imperfect properties). Following coining the share of each of the hazard criteria and their ratio importance indices One-way ONOVA test compared of the number of trees in different risk levels. Then, all the trees scored via the biased levels of their risk levels. Accordingly, based on the weighted scores, they were divided into five hazardous categories according. To develop an understanding of the quantitative variables, risk factors, the weight parameters and hazard classes, we carried out a principle component analysis (PCA) and a multi-layer perceptron (MLP) network procedure.
Results: The results from the proportion of each hazard index reviled the importance of the the importance of the structural tree weakness (61%), root problems (59%), and trunk and root wounds (55%). Also, results of One-way ONOVA test, showed the risk levels of the planted trees can be significantly classified into four classes of: with no risk or healthy, low, moderate and high risk classes, at one percent error level. The results of Duncan's mean test showed that the number of trees in no risk and low risk classes were significantly higher than the other classes at one percent error level. The results from the PCA indicated that the first and second components explained 41.40 percent of the total variation. The risk and weighting parameters of the wound on the trunk and root, contact power lines, root problems were highly and positively correlated. In general, the two variables of the root problems trunk as well as root wounding were among the most important variables in term of risk assessment of the plane trees. The high coefficient of determination values of training, validation, verification and finally all neural network data (0.927, 0.930, 0.930, and 0.927) and the least mean square error values (training data = 0.186, verification 0.196 and validation = 0.169) indicated, the accuracy desirability of the artificial neural network in the prediction of the risk classes of street side trees.
Conclusion: Root and wound problems have the greatest portion in the risk of Platanus orientalis L. and, based on the classification of trees, are currently in low and very low risk, but they are capable of becoming dangerous trees in the future. In general preventive and corrective measures are proposed for low and intermediate risk trees. Regarding the optimal performance of the Neural Network for the classification of the hazardous Platanus orientalis L. trees in the urban green space, it is introduced as a prediction model in evaluating the probability of fallen trees.
کلیدواژه‌ها [English]
)