TY - GEN
T1 - Artificial intelligence to model the potential distribution of Agave durangensis
AU - Escobar Flores, Jonathan Gabriel
AU - Sandoval, Y. Sarahi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We used four artificial intelligence algorithms; MaxEnt, climate space model (CSM), back propagation neural network (BPNN) and vector support machine (VSM) to model the potential distribution of Agave durangensis. In the field, 300 georeferenced records of agaves were obtained, for which information on 18 climates and three topographic variables was retrieved from geospatial databases. With the presence records and the variables, the 80% of the data was used for modeling and the remaining 20% was used to validate the model by estimating the receiver operating characteristic (ROC). Two models had an acceptable performance with ROC> 0.9. We observed that MaxEnt predicted agave distributions in canyons that did not correspond to the distribution of this species. The BPNN model predicts 95% of the areas that coincide with the natural distribution of the agaves. Therefore, the BPNN algorithm was the most accurate for predicting areas for agave repopulation.
AB - We used four artificial intelligence algorithms; MaxEnt, climate space model (CSM), back propagation neural network (BPNN) and vector support machine (VSM) to model the potential distribution of Agave durangensis. In the field, 300 georeferenced records of agaves were obtained, for which information on 18 climates and three topographic variables was retrieved from geospatial databases. With the presence records and the variables, the 80% of the data was used for modeling and the remaining 20% was used to validate the model by estimating the receiver operating characteristic (ROC). Two models had an acceptable performance with ROC> 0.9. We observed that MaxEnt predicted agave distributions in canyons that did not correspond to the distribution of this species. The BPNN model predicts 95% of the areas that coincide with the natural distribution of the agaves. Therefore, the BPNN algorithm was the most accurate for predicting areas for agave repopulation.
KW - Ecological Modelling
KW - Maxent
KW - Mezcal
KW - recovery population
UR - http://www.scopus.com/inward/record.url?scp=85140400804&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9883767
DO - 10.1109/IGARSS46834.2022.9883767
M3 - Contribución a la conferencia
AN - SCOPUS:85140400804
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5828
EP - 5831
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -