Seyed Mahdi Sadat Rasoul; Ebrahim Omidvar; Reza Ghazavi
Abstract
In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive ...
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In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive root systems allow them to store and direct significant amounts of water into the soil. The present study investigates the effect of rainfall amount and tree species on rainfall interception in Hashtgerd city of Alborz province during two seasons of winter 2017 and spring 2018. For this purpose, during seven rainfall events, the amount of throughfall was measured by the number of five rain gauges installed under each tree. In order to record rainfall events, a rain gage container was installed in a location that was sufficiently distant from buildings and trees, and rainfall events ranging from 2.1 to 6.8 mm were recorded. The results showed that the percentages of rainfall interception for spruce, apricot, fig, willow, walnut, and oak species were 44.6, 42.6, 36.4, 35.1, 33.6 and 30.4 percent, respectively. The results of statistical analysis showed that there is a significant difference among the values of rainfall interception in different tree species (P <0.01). Also, there is a significant difference among the rainfall interception in the rainfall classes (low (lower than 4 mm), medium (4-6 mm), and high (higher than 6 mm)) (P <0.01). Among the studied species, sparrow and apricot species have the highest rainfall interception, which it is possible to make more use of these two types in the control of runoff with urban planning.
Zahra Barati; Ebrahim Omidvar; Ataollah Shirzadi
Abstract
Landslide susceptibility mapping is considered as the first important step in landslide risk assessment. The main purpose of this study is to compare the performance of a machine learning algorithm (a logistic model tree), and a statistical model (a logistic regression), for landslide susceptibility ...
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Landslide susceptibility mapping is considered as the first important step in landslide risk assessment. The main purpose of this study is to compare the performance of a machine learning algorithm (a logistic model tree), and a statistical model (a logistic regression), for landslide susceptibility modeling in the Sarkhoon watershed, Chaharmahal and Bakhtiari province. For this purpose, at first, a landslide inventory map including a total of 98 landslide locations was constructed using historical landslides, and extensive field surveys. In addition, a total of 100 non-landslide locations were also identified to construct a database. The landslide and non-landslide locations were randomly selected and divided into two groups with a 70/30 ratio for modelling and validation processes. Twenty conditioning factors were selected based on literature review and geo-environmental properties in the study area. Subsequently, the logistic model tree (LMT) and the logistic regression (LR) models were applied to identify the influence of conditioning factors on landslide occurrence. Finally, the performance of the models in landslide susceptibility mapping was investigated using the area under the receiver operating characteristics curve (AUC). The results concluded that the LR model (AUC = 0.797) outperformed and outclassed the LMT (AUC = 0.740) model in the study area. Although both models were reliable tools for spatial prediction of landslide susceptibility; however, the LR model was more accurate that it can be proposed as an alternative tool for better management of areas prone to landslide in the study area.
