Mousumi Gupta

Abstract: This paper proposes a Neural Network model that has been utilized for image recognition. The main issue of Neural Network model here is to train the system for image recognition. In this paper the NN model has been prepared in MATLAB platform. The NN model uses Auto-Associative memory for training. The model reads the image in the form of a matrix, evaluates the weight matrix associated with the image. After training process is done, whenever the image is provided to the system the model recognizes it appropriately. The weight matrix evaluated here is used for image pattern matching. It is noticed that the model developed is accurate enough to recognize the image even if the image is distorted or some portion/ data is missing from the image. This model eliminates the long time consuming process of image recognition

Abstract: Several methodologies using Remote sensing and GIS are cited in the literature for landslide hazard assessment. Most of these methods need extensive mathematical modeling / simulation to evaluate the probability of occurrences of landslides. Proper methodology of assessing the landslide zone and the probability of occurrences of landslides will definitely be instrumental in landslide mitigation problem. In this paper an attempt has been made to assess the landslide hazard using a deterministic method. The study area has been chosen from Rangpo and Singtam along NH 31 A. The identified conditioning factors include soil, geology, forest, and drainage, and triggering factors such as slope and aspects are taken as input to fit into an aggregation model for assessment of landslide hazard. These probabilistic maps are compared with landslide maps generated from Google Earth from recent data (2007) for the accuracy of prediction. The generated hazard maps agree with the observed landslide occurrences. Thus the proposed methodology can be used in landslide hazard zonation prediction