Performance Tests to Modeling Future Climate–vegetation Interactions in Virtual World: an Option for Application of Remote Sensed and Statistical Systems
Author:
Hachmi Azeddine1, Zbiri Asmae1, Haesen Dominique2, El Alaoui-Faris Fatima Ezzahrae1, Vaccari David A.3
Affiliation:
1. Mohammed V University, Faculty of Science MOROCCO 2. Vlaamse Instelling Voor Technologisch Onderzoek (VITO) BELGIUM 3. Stevens Institute of Technology, Hoboken, NJ, Civil, Environmental and Ocean Engineering UNITED STATES
Abstract
Working in the virtual world is different to real experiment in field. Nowadays, with remote sensing and new analysis programs we can assure a quick response and with less costs. The problem is efficiency of these methods and formulation of an exact response with low errors to manage an environmental risk. The objective of this article is to ask question about performance of some tools in this decision making in Morocco. The study uses (Test 1: TaylorFit Multivariate Polynomial Regressions (MPR); Test 2: SAS Neural Network (NN) to modeling relationship between European Center for Medium-Range Weather Forecasts dataset and NDVI eMODIS-TERRA at arid Eastern Morocco. The results revealed that the both test could accurately predict future scenario of water stress and livstock production decrease. The experience shows that virtual work with Artificial Intelligence is the future of ecological modeling and rapid decision-making in case of natural disasters.
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
Computer Science Applications,Information Systems
Reference39 articles.
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