Toward prediction of land subsidence assisted by artificial intelligence approaches

Land subsidence referring to the lowering of Earth’s land surface poses destructive threats to buildings and infrastructures and increases vulnerability to floods. The tendency to overexploit groundwater resources due to ever-increasing demand for water in urban areas is known as one of the main drivers for land subsidence, especially in regions with compressible sediments or formations susceptible to changes in groundwater pressure. Land subsidence has been observed in many countries around the globe including but not limited to USA, Mexico, Spain, Italy, Saudi Arabia, Iran, India, Vietnam, and China.

Artificial intelligence (AI) and machine learning algorithms prove to be of great values to assess and predict a variety of hydrological and environmental dynamics and trends (Hassani et al., 2021; Mahdaviara et al., 2022). Leveraging on this opportunity, we develop a new framework, assisted by AI approaches, to quantify and predict how various environmental and climatic parameters influence the occurrence and extent of land subsidence. We show the general applicability of the proposed framework through the case of land subsidence observed in Iran, i.e. a semi-arid to arid country which strongly relies on the limited groundwater resources for a wide range of activities. The country hosts some of the fastest-sinking cities in the world. As a case study, we focused on the land subsidence observed in Varamin plain located in central Iran with an average annual precipitation of 420 millimeters and 210 millimeters of subsidence per year in the last 20 years.  A combination of the field and satellite data over the last two decades was prepared for the training of the models. In the next level, the training matrix was exposed to the AI algorithms aiming to develop models relating the land subsidence rate to a variety of environmental and climatic factors. Our preliminary analysis suggests that the groundwater withdrawal and precipitation rate are among the most important parameters affecting the rate of subsidence. The modelling tools will be used to detect the potential hotspots for land subsidence under different water management and climate change scenarios in other places. This will be helpful for preventing the forthcoming damages and devising the necessary action plans to mitigate the situation under different conditions.

 

References

Hassani, A., Azapagic, A., Shokri, N. (2021). Global Predictions of Primary Soil Salinization Under Changing Climate in the 21st Century, Nat. Commun., 12, 6663. doi.org/10.1038/s41467-021-26907-3.

Mahdaviara, M., Sharifi, M., Bakhshian, B., Shokri, N. (2022), Prediction of Spontaneous Imbibition in Porous Media Using Deep and Ensemble Learning Techniques, Fuel, 329, 125349.