| Deep Learning in IoT: An LSTM Approach for NDVI Forecasting |
| Paper ID : 1148-ICEEM2023 (R1) |
| Authors |
|
Sayed kenawy *1, Khaled Sherif2, Mohamed Azmy3, Khder Alakkari4, Mostafa Abotaleb5 1Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology 2Department of Communications and Electronics Delta Higher Institute of Engineering and Technology, Mansoura, Egypt 3Department of Civil Engineering Delta Higher Institute for Engineering and Technology, Mansoura 35111, Egypt 4Department of Statistics and Programming Faculty of Economics, University of Tishreen Tartous, Syria 5Department of System Programming South Ural State University, Chelyabinsk 454080, Russia |
| Abstract |
| This study presents a novel application of a Long Short-Term Memory (LSTM) deep learning model for timeseries analysis of the Normalized Difference Vegetation Index (NDVI) from January 1, 1984, to April 21, 2023. As remote sensing technologies generate substantial environmental data, advanced analytics like LSTM provide essential tools for precise interpretation and forecasting. Through grid search optimization, hyperparameters were fine-tuned for optimal LSTM performance. The NDVI mean value over the study period is 0.332, indicative of a moderate vegetation presence. The data series’ stationarity, confirmed through the Dickey-Fuller test, contributes to accurate prediction outcomes. The LSTM model demonstrates superior predictive performance, evidenced by the Root Mean Squared Error (RMSE) values of 0.000764 and 0.000900 for the training and testing datasets respectively. The high R-squared and correlation values further substantiate its efficacy. This study paves the way for leveraging LSTM models in large-scale NDVI data analysis, contributing to environmental monitoring, climate change tracking, and vegetation health assessments. Future work can extend this model to other remote sensing indices and explore various deep learning architectures for enhanced predictive accuracy. |
| Keywords |
| Forecasting, Mathematical model, Grid search, NDVI, dynamic system, Iot, and crops |
| Status: Accepted |