A Hybrid Clustering-Fusion Methodology for Land Subsidence Estimation

A 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 input-output 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. The five SC models include Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) neural network and two optimized models, namely, Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN). To fuse individual SC models, three methods including Genetic Algorithm (GA), K-Nearest Neighbors (KNN) and Ordered Weighted Average (OWA) based on ORNESS method and ORLIKE method, are developed and evaluated. Results show that the fusion-based method is significantly superior to each of the employed individual methods in predicting land subsidence rate

Monitoring of ground deformation in Liulin district, China using InSAR approaches

Coalbed methane (CBM) exploration generally refers to a technique that extracts natural gas from coal beds. The development of CBM in Liulin, China, has experienced a significantly growth period during the past two decades. Previous research mainly focused on the coal geological background or CBM technique itself, while time series InSAR (TS-InSAR) technique was conducted in this work to study the potential land deformation induced by CBM extraction from 2003 to 2011. In total, 21 ALOS-1 PALSAR images (acquired from 22 December 2006 to 2 January 2011) and 14 ENVISAT ASAR scenes (captured between 29 October 2003 and 7 November 2007) were used. The TS-InSAR outcome revealed that the annual deformation rates were ranging from 15 to −40 mm yr−1 over the study region. Then the time series deformation evolutions were analysed over 8 CBM sites (No. 4 coal seam) out of 20, and the subsidence rates between 1.9 and −6.5 mm yr−1 were derived. In addition, the average subsidence rate and standard deviation among these eight measurements were −3.0 and 2.6 mm yr−1 respectively, suggesting that these CBM extraction sites were quite stable and no obvious subsidence had been observed during this eight-year period