@article {611286,
	title = {Flood prediction and  risk assessment using advanced geo-visualization and data mining techniques},
	journal = {World Scientific and Engineering Academy and Society | APPLIED COMPUTATIONAL SCIENCE},
	volume = {20},
	year = {2014},
	abstract = {Throughout the last century, flooding has been one of the most costly natural disasters concerning of human casualties, property damage, and environment degradations. Flooding is a complex natural phenomenon which is highly constrained by the geospatial environment where it evolves. The need for flood prediction and risk assessment is increasing, and decision makers still lack intelligent tools to study flooding. Artificial intelligence and knowledge discovery advances offer approaches for the modeling and simulation of such complex phenomena. To this extent, we propose to build a computer simulation platform to support flood prediction and risk assessment using advanced geo-visualization and data mining techniques. The outcomes and results of our simulations aim to better manage floods through prevention, protection, and emergency response perspectives.},
	url = {http://www.wseas.us/e-library/conferences/2014/Malaysia/ACACOS/ACACOS-02.pdf},
	author = {Dr. Omar Al-Azzam and Dr. Mehdi Mekni and Deli Sarsar and Kirubel Seifu}
}
