AIRS: A QGIS plugin for time series forecasting using deep learning models

Abstract

Time series forecasting, particularly when applied to geospatial data, serves as an essential tool for accurate observation and prediction of environmental and spatial occurrences. Recently, the integration of deep learning models into forecasting processes has garnered increased importance. Deep learning methods offer enhanced capabilities for identifying complex patterns within geospatial datasets, leading to more accurate forecasts. Nevertheless, there is a critical need for analyzing and identifying effective deep learning models in order to assure the accuracy of forecasting outcomes. This study presents an open-source QGIS plugin named AIRS (Artificial Intelligence forecasting Remote Sensing). This plugin allows time series forecasting using five deep learning models (i.e., FFNN, single LSTM, stacked LSTM, BiLSTM, and Conv-LSTM) and provides a user-friendly tool permitting data processing, model building and training, future prediction, accuracy analysis, and results visualization and saving. AIRS is written in Python using QGIS internal and external packages, with an easy-to-use GUI interface.