Grnn Based Modelling of Pier Scour Depth Using Field Dataset

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications

We proposes a simple deep learning architecture combining elements of Inception, ResNet and Xception networks. Four new datasets were used for classification with both small and large training samples. Results in terms of classification accuracy suggests improved performance by proposed architecture in comparison to Bayesian optimised 2D-CNN with small training samples. Comparison of results using small training sample with Indiana Pines hyperspectral dataset suggests comparable or better performance by proposed architecture than nine reported works using different deep learning architectures. In spite of achieving high classification accuracy with limited training samples, comparison of classified image suggests different land cover classes are assigned to same area when compared with the classified image provided by the model trained using large training samples with all datasets.Comment: 22 Page, 6 tables, 9 Figure

Air Quality Mapping and Urban Planning for Sustainable Urban Ecology: A Case Study of Chandigarh, India

In the fast urbanizing world, it has become vital to study urban ecology so as to understand where and how anthropogenic activities impair the urban environment, including air quality; and how living conditions can be improved by urban planning without mortifying urban ecology. This may require innovative technological ideas to efficiently and judiciously utilize the limited urban space. Air quality mapping using Geographic Information System (GIS) provides one such effective tool to urban planners to identify and target specific areas for air quality management in urban setting. In the present study, the air quality mapping of a well-planned city of Chandigarh (India) with proper environmental management zoning has revealed that the air quality index (AQI) of the city falls under “Moderately Polluted (101-200)” category primarily due to annual average concentrations of  (range: 44.17-68.87; overall: 56.64) and  (range: 99.32-129.39; overall: 111.92) being higher than the permissible levels of  40   and 60  respectively as per Indian standards at all locations as well as for overall city. The study has identified vehicular traffic as the primary reason responsible for the moderately polluted air quality of the city that has the highest vehicle density (878 per 1,000 population) in India. The paper has also suggested measures that may be incorporated during designing and developing the physical and social infrastructures in the city so as to judiciously and efficiently utilize the limited urban space

Factors influencing the accuracy of remote sensing classifications: a comparative study

Within last 20 years, a number of methods have been employed for classifying remote sensing data, including parametric methods (e.g. the maximum likelihood classifier) and non-parametric classifiers (such as neural network classifiers).Each of these classification algorithms has some specific problems which limits its use. This research studies some alternative classification methods for land cover classification and compares their performance with the well established classification methods. The areas selected for this study are located near Littleport (Ely), in East Anglia, UK and in La Mancha region of Spain. Images in the optical bands of the Landsat ETM+ for year 2000 and InSAR data from May to September of 1996 for UK area, DAIS hyperspectral data and Landsat ETM+ for year 2000 for Spain area are used for this study. In addition, field data for the year 1996 were collected from farmers and for year 2000 were collected by field visits to both areas in the UK and Spain to generate the ground reference data set. The research was carried out in three main stages.The overall aim of this study is to assess the relative performance of four approaches to classification in remote sensing - the maximum likelihood, artificial neural net, decision tree and support vector machine methods and to examine factors which affect their performance in term of overall classification accuracy. Firstly, this research studies the behaviour of decision tree and support vector machine classifiers for land cover classification using ETM+ (UK) data. This stage discusses some factors affecting classification accuracy of a decision tree classifier, and also compares the performance of the decision tree with that of the maximum likelihood and neural network classifiers. The use of SVM requires the user to set the values of some parameters, such as type of kernel, kernel parameters, and multi-class methods as these parameters can significantly affect the accuracy of the resulting classification. This stage involves studying the effects of varying the various user defined parameters and noting their effect on classification accuracy. It is concluded that SVM perform far better than decision tree, maximum likelihood and neural network classifiers for this type of study. The second stage involves applying the decision tree, maximum likelihood and neural network classifiers to InSAR coherence and intensity data and evaluating the utility of this type of data for land cover classification studies. Finally, the last stage involves studying the response of SVMs, decision trees, maximum likelihood and neural classifier to different training data sizes, number of features, sampling plan, and the scale of the data used. The conclusion from the experiments presented in this stage is that the SVMs are unaffected by the Hughes phenomenon, and perform far better than the other classifiers in all cases. The performance of decision tree classifier based feature selection is found to be quite good in comparison with MNF transform. This study indicates that good classification performance depends on various parameters such as data type, scale of data, training sample size and type of classification method employed

Anti-termite activity of essential oil and its components from Myristica fragrans against Microcerotermes beesoni

The essential oil obtained by hydrodistillation of the fruits of Myristica fragrans was analyzed by GC and GC-MS. Twenty eight compounds were identified representing 95.9% of the oil. The major constituents of the oil were α-pinene (6.4%), Sabinene (37.7%), β-pinene (7.3%), myrcene (3.5%),Limonene (4.7%),Terpine-4-ol (5.8%), safrole (3.4%) and myristicin (6.8%).The essential oil and its major constituents were evaluated at different dilution against Microcerotermes beesoni, test termite. The LC50 value of fruit essential oil is 28.6 mg/g. Furthermore, exposure to myristicin caused 100% mortality at a dosage of 5 mg/g after 14d. @JASEMJ. Appl. Sci. Environ. Manage. Sept, 2011, Vol. 15 (3) 559 - 56