Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions.

In the development and production of oilfields, oil and gas gathering and transportation pipelines play a pivotal role, with their safe and stable operation being crucial for energy transmission. The environmental conditions and geological disasters along the pipeline routes pose significant threats to pipeline integrity. Existing research often fails to adequately consider the characteristics of oil and gas pipelines as entities that endure such disasters, as well as the potential impacts of surrounding geological disasters and ground deformations. This study establishes a comprehensive failure probability assessment framework aimed at evaluating the susceptibility to disasters, environmental factors, and potential ground deformations along pipeline routes. By employing DS-InSAR technology, we account for the effects of ground deformation and conduct an in-depth analysis of the vulnerability and susceptibility to geological disasters along a pipeline. These assessments are integrated using a failure probability matrix method, resulting in a failure probability level distribution map for the pipelines. In this study, we applied the framework to the Ordos Basin in China. The insights and framework offer a comprehensive understanding for large-scale oil and gas pipeline failure probability assessment, aiding relevant authorities in precisely grasping the impacts of disasters, environmental conditions, and their changes on pipelines, enabling the identification of management priorities and the formulation of more accurate protective measures.