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Study area of Jhuoshuei River Alluvial Fan, Taiwan, and distribution of leveling points and MCMWs (From Wang, 2015).
Source publication
The Taiwan Water Resources Agency uses four techniques to monitor subsidence
in Taiwan, namely data from leveling, global positioning system (GPS),
multi-level compaction monitoring wells (MCMWs), and interferometry
synthetic aperture radar (InSAR). Each data type has advantages and
disadvantages and is suitable for different analysis tools. Only M...
Context in source publication
Context 1
... River Alluvial Fan, located in central Taiwan (see Fig. 1), is the largest alluvial fan in Taiwan. Wu River and Beigang River flow through the north and south areas, respectively, and Jhuoshuei River flows across the middle of the alluvial fan. Changhua and Yunlin counties are located in the north and south divisions of Jhuoshuei River Allu- vial Fan, respectively. The western boundary is the ...
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Citations
Land subsidence (LS), which mainly results from poor watershed management, is a complex and non-linear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road, or fault, rainfall, geology, and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANN- and GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern, and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR data, 39%, 20%, 25%, 13%, and 3% of the investigated areas are subject to a yearly LS of -1 to -2.5, -2.5 to -5, -5 to -7.5, -7.5 to -10, and -10 to -20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km2) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan, and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future.
