Hydromorphological features of rivers are important to determine the quality of water body, its evolution due to nearby landscape changes and possible impact to the ecosystem. On the one hand, the directly measured indicators of hydromorphological features are accurate, but obtained by a costly and time-consuming process. Thus, it can be performed only on short segments of river. On the other hand, using indirect measurement techniques results in a lower accuracy but significantly lower measurement costs, manpower and time needed. The obvious advantage of remote sensing techniques is processing data acquired in the regions that are difficult to access. Moreover, automated data processing enables to observe large river sections and detect changes, assess quality of water body, and make timely decisions to prevent undesirable processes. The project research covers a variety of scientific disciplines, namely, hydrology, computer vision and image processing, numerical simulation, optimization. The project aims to develop a combined physical behavior and artificial intelligence (AI) model to determine hydromorphological features of the low-land river by using multi-spectral images collected with unmanned aerial vehicle (UAV) as input. The model is based on the inverse modeling approach as an additional layer is incorporated to the AI model to ensure the consistency of the predicted results and physical laws. The main tasks considered in the project research refer to preparation of the dataset composed of multispectral aerial images and hydromorpholigical features collected by direct measurement techniques, creating the numerical hydrodynamic model, training computer vision model of suitable architecture and incorporation AI and hydrodynamic models. The practical value of the model is based on the AI model consistent with physical laws and practically acceptable indicators of hydromorphological features.
Project funding:
Projects funded by the Research Council of Lithuania (RCL), Projects carried out by researchers’ teams
Period of project implementation: 2023-04-01 - 2026-03-31
Project coordinator: Kaunas University of Technology
Project partners: Lithuanian Energy Institute
