Explainable Artificial Intelligence in Forecasting MT-InSAR Based Time Series Surface Movements

Abstract

Abstract. Explainable artificial intelligence (XAI) enables users to interpret the black box of machine learning (ML) algorithms and its applicability across various ML algorithms allows for the investigation of feature impacts on the model. Among the ML algorithms, Long Short Term Memory (LSTM) deep learning method has become popular in various applications, especially forecasting analysis, due to its ability to effectively capture long-range temporal dependencies in sequential data, as is often required when analyzing time-series deformation patterns derived from multi-temporal interferometric synthetic aperture radar (MT-InSAR). The SHapley Additive exPlanations (SHAP) method, one of the most popular XAI techniques, has been widely used to identify the impacts of features on processes. To this end, forecasting analysis of MT-InSAR-based time series surface movements was performed using the LSTM method in the two selected case regions at Istanbul Airport. These case regions exhibit different time series characteristics (subsidence and stable) and belong to different surface types (runway and building). According to the results obtained, the LSTM method showed successful performances with RMSE, MAE, and R values of 1.12 mm, 0.92 mm, 0.672 for case 1 and 1.37 mm, 1.13 mm, 0.385 for case 2. To assess the impacts of the exogenous variables, including trend, seasonal, and residual components of time series data and meteorological parameters gathered from the ERA5-Land dataset, were investigated using the SHAP method, and results were evaluated specifically for each case region.