An Artificial Neural Network-Based Snow Cover Predictive Modeling in the Higher Himalayas

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network(ANN), was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from- HadCM3, a global circulation model to project future climate scenario, under the A1 B emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2011 to 2040. The 4700 m to 5200 m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantial changes due to the impact of climate change.

Optimizing artificial neural network-based indoor positioning system using genetic algorithm

The Global Positioning System(GPS)is expected to play an integral role in the development of digital earth;however,the GPS cannot provide positioning information in regions where a majority of the population spends their time,that is,in urban and indoor environments.Hence,alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital earth.Wi-Fi-based positioning systems(WPS)stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor environments.The WPS-based fingerprinting is the most widely adopted technique for position determination,but its accuracy is lower than that of techniques such as time of arrival and angle of arrival.Improving the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi signals.Here,a novel server-based,genetic-algorithm-optimized,cascading artificial neural network-based positioning model is presented.The model is tested in 2D and 3D indoor environments under varying conditions.The model is thoroughly investigated on a real Wi-Fi network,and its accuracy is found to be better than that of other well-known techniques.A mean accuracy of 1.9 m is achieved with 87%of the distance error within the range of 0-3 m.