6533b7d0fe1ef96bd125b691

RESEARCH PRODUCT

Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia

Beatriz MartínezManel RhifImed Riadh FarahAli Ben Abbes

subject

Mean squared errorSeries (mathematics)business.industryDeep learningNormalized Difference Vegetation IndexPearson product-moment correlation coefficientsymbols.namesakeNorth westStatisticssymbolsmedicineArtificial intelligenceTime seriesmedicine.symptombusinessVegetation (pathology)Mathematics

description

The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (RMSE) and the highest Pearson correlation coefficient (R) of 0.034 and 0.93, respectively.

yearjournalcountryeditionlanguage
2020-03-012020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
https://doi.org/10.1109/m2garss47143.2020.9105149