Evaluation of erosion risk map based on hierarchical decision tree method, a case study: Semnan drainage basin

Introduction: Today, Soil erosion has become one of the biggest problems in the country, especially in arid and semi-arid including Semnan. Effective and long term water and soil conservation programs require the concentration of resources on limited areas. For that purpose, regional-scale assessments of erosion risk are required. There are various methods to studying, evaluating, calculate and prevention with soil erosion. In addition, a number of parameters such as lithology, slope, aspect, land cover, elevation, and distance to stream, drainage density, vegetable cover, land use, river banks, and human activities are recommended to analyze the mechanism of soil erosion. So a rapid and cost effective methodological erosion assessment for these regions is required to describe and monitor the processes that control erosion. This study uses one of the remote sensing analyses to describe the contribution of several factors that control erosion in Semnan drainage basin where erosion is the major environmental problem. Remote sensing monitoring has been carried out by using aero photos, or multispectral images, DTM (Digital Terrain Model) or ALS (Airborne LASER Scanning) data. Semnan basin, study area, is located in north of Kavir plain and south of Alborz mountain range. Methodology: This study was conducted to evaluate the potential of analyzing regional erosion risk Topography, land use; vegetation density, soil properties and climatic proxies are used to determine erosion risk and to provide basic maps of water and soil conservation practices. A hierarchical decision tree is used to sum and combine the weight of parameters controlling the erosion. The assigned weights of each spatial unit express the susceptibility to erosion. The most important attributes in the definition of erosion landforms like gullies were selected using decision tree induction algorithms, being these attributes spectral, altimetry and texture. Classifications hierarchical and by decision trees were carried out. Using decision tree the classification is performed only by a factor of scale, not allowing the identification of all the constituent features of the erosion landforms system. One of the advantages of this method is that it can be used if there are insufficient experimental data. The lack of experimental data can be compensated for through the use of expert evaluations. Results and discussion: Three different combinations of the three dominant controlling factors are yielded in this study. In order to optimize the qualitative erosion risk assessment, each combination is discussed and evaluated depending on the contribution of parameters involved in the erosion process. As different erosion landforms erosion is similar when presents the same evolution stage and soil type, it is not possible to select attributes to classify all erosion landform systems, being necessary to investigate attributes for each erosion landform erosion, based on available data and existing land use classes in the area. The erosion landforms are the biggest erosive processes and, consequently, responsible for ambient, social and financial damages. Corrective and preventive measures need mapping and monitoring, which can be made by local measurements or by remote sensing. In relation to the remote sensing, the erosion landform erosion presents spectral heterogeneity (soil, vegetation, shade and water mix), spatial heterogeneity (existence of features as head, canals and digits with irregular forms and variable dimensions) and altimetry variation (with high declivity on the edges). Due to spectral heterogeneity, it is not enough use only spectral data, being necessary auxiliary data, as altimetry and texture data. This clearly shows that the study area is generally exposed to a high hazard of soil erosion. Nevertheless, there is a probability that the rate of erosion will increase in the future, as hazard is the probability of occurrence of a potential damaging phenomenon, within a period of time and a given area. As known, there is always an interest to depend on latest developments when making subjective judgments. In spite of the results obtained in this study, the development of a susceptibility map is usually determined by the needs and available resources, and AHP method can be equally important for all sorts of susceptibility zoning practices. Conclusion: The purpose of this study was to assess the soil erosion hazard in the Semnan province for planning appropriate conservation measures. The integrated GIS-AHP model was used to define spatial distribution of soil erosion hazard. In this area, erosion risk mainly was related with vegetation and also, it anticipated that the southern and south-eastern region due to the poverty of vegetation associated with increased levels of erosion. In each of the three mapped models, the area of the class with high erosion sensitivity was more than 75% and for observational data, the area in all three maps is above 71%. Also, the results of the assessment show that in all three maps there are over 99% correlation between the data obtained from the modeling and the test data. The erosion landforms present spectral and spatial heterogeneity and altimetry variation. This research demonstrates that the model developed was an effective tool for fast assessment of soil erosion hazard by the integration of remote sensed data, AHP, and GIS techniques. Nevertheless, the results obtained in this study are valid only for generalized planning and assessment purposes. They may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail. Finally, any proposed decision-making tool in erosion control studies should also include local experimentation data to better simulate the erosion hazard, resulting thereby in the most appropriate and efficient choice of soil conservation works

Effect of Slope Position on Soil Properties and Types Along an Elevation Gradient of Arasbaran Forest, Iran

Sustainable development by forest managing need to identify forest ecosystem elements. Forest soil is the most important element of forest ecosystem that has key roles in forest managing. Therefore, studying of soil properties and evolution under different environmental conditions is necessary for sustainable management of forest ecosystems. Spatial variation of soil properties is significantly influenced by some environmental factors that slope position is one of them. The aim of this study was evaluating effects of slope position on forest soil change which was carried out in Arasbaran forest, North-West of Iran. Nine soil profiles were dug, described and sampled in three different parts of an altitudinal transect with same environmental conditions and different slope positions. Then soil samples were analysed physicaly and chemicaly and so classified based on Soil Taxonomy 2014. Also according to obtained results One-way analysis of variance was used to test relations of soil properties and slope positions. This results revealed significant effect of slope positions on thickness of the soil profile and solum, clay, organic carbon and total nitrogen percentages and cation exchange capacity at 5% level of confidence which lead to change of type, depth and sequence of soil horizons along altitudinal transect. Finally, it has found that slope position not only has important role in soil properties changes and soil evolution but also it can't be refused the various role and influence of same forest stand in different slope positions. Therefore various soils such as Inceptisols, Alfisols and Molisols were observed under different slope positions. Then it can be achieved that, because of special forest vegetation, soil evolution along altitudinal transect of forest ecosystems are differing from other ecosystems. Thus, for forest soil management program it is necessary to consider both of topography and vegetation effect over the area, even if one of them is constant

Macro Determinants of Infant Mortality in ECO Countries:Evidence from Panel Data Analysis

Abstract Introductio

Climate Change Assessment in the basin of Hamoon International Wetlands Using LARS-WG6 Model

The aim of this study is to evaluate the status of climatic variables in the basin of Hamoun International Wetlands using different general circulation models of LARS-WG6 downscaling method, under emission scenarios RCP4.5 and RCP8.5 during 2040-2021, 2060-2041 and 2080-2061 based on observed parameters in Zabol Synoptic gauge in 2019-1983. Accuracy analyzing indicated a high correlation between simulated and observed data. The results of downscaling showed that the mean minimum and maximum temperatures will increase in all months under two scenarios in all models during 2021-2080. The upward trend will be more severe in the period 2061-2080 compared to previous periods. The maximum and minimum increase in the mean minimum and maximum monthly temperatures are predicted in HadGEM2-EC model, RCP8.5 scenarios and MPI-ESM-MR model, RCP4.5 scenarios, respectively. During 2080-2021, the range of monthly maximum temperature changes in RCP4.5 and RCP8.5 scenarios will be 0.29-2.85 and 0.54-5.80 degrees Celsius, respectively, and the range of monthly minimum temperature changes will be 0.18 -5.51 and 0.61-5.38 degree Celsius respectively. The average monthly rainfall is projected to fluctuate in different models and scenarios. The average monthly precipitation changes under different models and scenarios will be between -3.68-6.6 mm. The highest increase in the average monthly rainfall will happen in March based on HadGEM2-EC model in the RCP4.5 scenarios by 8.6 mm in 2060-2041. The highest decrease in the average monthly rainfall is predicted in January by the MIROC5 model in the RCP4.5 scenarios by 3.68 mm in 2080-2061.The results of this study can be useful for natural resources managers in setting up climate-adoptive livelihood strategies and agricultural practices

Types of neural guides and using nanotechnology for peripheral nerve reconstruction

Peripheral nerve injuries can lead to lifetime loss of function and permanent disfigurement. Different methods, such as conventional allograft procedures and use of biologic tubes present problems when used for damaged peripheral nerve reconstruction. Designed scaffolds comprised of natural and synthetic materials are now widely used in the reconstruction of damaged tissues. Utilization of absorbable and nonabsorbable synthetic and natural polymers with unique characteristics can be an appropriate solution to repair damaged nerve tissues. Polymeric nanofibrous scaffolds with properties similar to neural structures can be more effective in the reconstruction process. Better cell adhesion and migration, more guiding of axons, and structural features, such as porosity, provide a clearer role for nanofibers in the restoration of neural tissues. In this paper, basic concepts of peripheral nerve injury, types of artificial and natural guides, and methods to improve the performance of tubes, such as orientation, nanotechnology applications for nerve reconstruction, fibers and nanofibers, electrospinning methods, and their application in peripheral nerve reconstruction are reviewed

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum