TY - JOUR
T1 - Performance tests to modeling future climate-vegetation interactions in virtual world
T2 - an option for application of remote sensed and statistical systems
AU - Hachmi, Azeddine
AU - Zbiri, Asmae
AU - Haesen, Dominique
AU - El Alaoui-Faris, Fatima Ezzahrae
AU - Vaccari, David A.
N1 - Publisher Copyright:
© 2021 World Scientific and Engineering Academy and Society. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Decision making
KW - ECMWF Rainfall
KW - Eastern Morocco
KW - Multivariate Polynomial Regressions (MPR); Neural Network (NN)
KW - NDVI
KW - Performance Tests
KW - Virtual world
UR - http://www.scopus.com/inward/record.url?scp=85126123549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126123549&partnerID=8YFLogxK
U2 - 10.37394/23209.2021.18.22
DO - 10.37394/23209.2021.18.22
M3 - Article
AN - SCOPUS:85126123549
SN - 1790-0832
VL - 18
SP - 178
EP - 189
JO - WSEAS Transactions on Information Science and Applications
JF - WSEAS Transactions on Information Science and Applications
ER -