Effect of Topographic Data Accuracy on Watershed Management.

A digital elevation model (DEM) is a digital model or 3D representation of a terrain's surface. There are many methods to create DEM such as LiDAR, stereo photogrammetry and topographic maps. DEMs are very important for many applications such as extracting terrain parameters for geomorphology and modeling water flow for hydrology or mass movement. A number of websites are available to provide DEM such as SRTM, GTOPO30 and ASTER GDEM but their accuracy differs from one to another and also selecting a small DEM size (high resolution) gives accurate information, but the analysis takes long time. This paper aims to analyze the impact of using different available DEMs on watershed geomorphological properties on order to provide guidelines for users to select the most suitable DEM that obtain an accurate analysis in less time. Three programs; watershed modeling systems: WMS, Global Mapper and Google Earth were used in this study. Three case studies were studied to check the accuracy of these models and select the most accurate one for application. Satellite images downloaded from Google Earth were used as a guide reference for the comparison due to their accuracy and high resolution. The results indicated that the SRTM model was more accurate (95%) for all case studies according to our comparison between its delineation and satellite images. ASTER GDEM is the second most accurate model with an accuracy of 87%, the GTOPO30's accuracy is 80%.

Artificial intelligence in public health prevention of legionelosis in drinking water systems.

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives.