A hybrid clustering-fusion methodology for land subsidence estimation

Abstract

This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Natural Hazards, published by Springer. Copyright restrictions may apply. AcknowledgmentsThe authors would like to thank Dr. Maryam Dehghani for providing the dataset used in this study. AbstractA hybrid clustering-fusion methodology is developed in this study that employs Genetic Algorithm (GA) optimization method, k-means method, and several soft computing (SC) models to better estimate land subsidence. Estimation of land subsidence is important in planning and management of groundwater resources to prevent associated catastrophic damages. Methods such as the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) can be used to estimate the subsidence rate, but PS-InSAR does not offer the required efficiency and accuracy in noisy pixels (obtained from remote sensing). Alternatively, a fusion-based methodology can be used to estimate subsidence rate, which offers a superior accuracy as opposed to the traditionally used methods. In the proposed methodology, five SC methods are employed with hydrogeological forcing of frequency and thickness of fine-grained sediments, groundwater depth, water level decline, transmissivity and storage coefficient, and output of land subsidence rate. Results of individual SC models are then fused to render more accurate land subsidence rate in noisy pixels, for which PS-InSAR cannot be effective. We first extract 14,392 different inputoutput patterns from PS-InSAR technique for our study area in Tehran province, Iran. Then, k-means method is used to divide the study area to homogenous zones with similar features.

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