The importance of services, based on current location of objects is growing. This is because Global Navigation Satellite System (GNSS) cannot provide object position inside buildings. In modern era the Location Based Services (LBS) are tremendously dependent on Indoor Positioning System (IPS). Parallel RPROP and greedy algorithm were combined for development of IPS using Received Signal Strength (RSS) in heterogeneous environment, the environment comprised of human activity, walls material, cupboards, and various type of surveying machines etc. The propagation of Wi-Fi signal varies directionally, therefore to cope with direction changes in signals; this proposed model produces three sets of weights, which could be considered best for easting, northing and height respectively. Proposed model was trained with 75% of collected data and tested on remaining 25% data. Distance error between known points and predicted coordinates was used for accuracy assessment. Through experiments a maximum accuracy of 0.87m was achieved and it was found that median error was less than mean error. Median error between known points and predicted coordinates was about 3.32m and their mean error was about 4.62m, which is satisfactory as far as 3D position determination is concerned. On the basis of results the use of parallel RPROP and greedy algorithm for 3D position determination in heterogeneous environment is recommended.

Floods are the most frequent and destructive natural disasters causing damages to human lives and their properties every year around the world. Pakistan in general and the Peshawar Vale, in particular, is vulnerable to recurrent floods due to its unique physiography. Peshawar Vale is drained by River Kabul and its major tributaries namely, River Swat, River Jindi, River Kalpani, River Budhni and River Bara. Kabul River has a length of approximately 700 km, out of which 560 km is in Afghanistan and the rest falls in Pakistan. Looking at the physiography and prevailing flood characteristics, the development of a flood hazard model is required to provide feedback to decision-makers for the sustainability of the livelihoods of the inhabitants. Peshawar Vale is a flood-prone area, where recurrent flood events have caused damages to standing crops, agricultural land, sources of livelihood earnings and infrastructure. The objective of this study was to determine the effectiveness of the ANN algorithm in the determination of flood inundated areas. The ANN algorithm was implemented in C# for the prediction of inundated areas using nine flood causative factors, that is, drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use. For the preparation of spatial geodatabases, thematic layers of the drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use were generated in the GIS environment. A Neural Network of nine, six and one neurons for the first, second and output layers, respectively, were designed and subsequently developed. The output and the resultant product of the Neural Network approach include flood hazard mapping and zonation of the study area. Parallel to this, the performance of the model was evaluated using Root Mean Square Error (RMSE) and Correlation coefficient (R2). This study has further highlighted the applicability and capability of the ANN in flood hazard mapping and zonation. The analysis revealed that the proposed model is an effective and viable approach for flood hazard analysis and zonation.