Remote sensing-AI based prediction of subsidence in Ararat Valley, Armenia Modeling Ground Subsidence Using InSAR, LSTM Networks, and Remote Sensing Data
Year: 2024
Type: dissertation
Abstract: The Ararat Valley, being the cereal basket of Armenia and Turkey, is in danger with over-abstracted groundwater, which already induces colossal water loss and land subsidence. In the current work, advanced remote sensing techniques and machine learning models are applied to predict subsidence and give datadriven recommendations to effect sustainable water management. The tracking of terrain changes and analysis and prediction of the patterns of subsidence are carried out with the help of Synthetic Aperture Radar Interferometry and Long Short-Term Memory networks, respectively. The integration of InSAR data with LSTM models allows an in-depth analysis of subsidence dynamics, forming a basis for forecasting and management strategies. In that regard, it is early results confirmed the expediency of using the combination of remote sensing data and machine learning techniques in stating that it is possible to monitor subsidence accurately for all zones in the Ararat Valley. Further study investigates the socio-economic effects that subsidence will have and will give practical recommendations in relation to water management practice improvements which include enforcement of water reuse regulations, enhance the public awareness on the same, and adjustments in water pricing to its real value. By addressing both technical and socio-economic aspects of water management, the current research opens new horizons in providing a truly integrative system for coping with both land subsidence and securing the sustainable use of water resources in the Ararat Valley. It is considered a sign that new and very promising opportunities are related to uniting innovative technologies with large, comprehensive, and far-reaching policy measures to effectively address environmental challenges.
Author: Romano, Giuseppe
Institution: Lund University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204014087