Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the nonlinear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180-218 days band (∼ 6-7 months) from
A comparison of the ability of artificial neural networks and polynomial fitting was carried out in order to model the horizontal deformation field of the Cascadia Subduction Zone, as determined from GPS analyses of the Pacific Northwest Geodetic Array (PANGA).One set of data was used to calculate the unknown parameters of the model (training set 75 % of the whole data set) and the other set was used only for testing the accuracy of the derived model (testing set -25 % of the whole data set). The testing set has not been used to determine the parameters of the model.The problem of overfitting (see Kecman (2001)) (i.e., the substantial oscillation of the model between the training points, the same problem than polynomial wiggle (Mathews and Fink 2004) can be avoided by restricting the flexibility of the neural model. This can be done using an independent data set, namely the validation data (one third part of the training set). The proposed method is the so-called "stopped search method", which can be used for obtaining a smooth and precise fitting model. However, when fitting high order polynomials, it is difficult to overcome the negative effect of the overfitting problem.Different order polynomial models and neural network models with different numbers of neurons were calculated. The best fitting polynomial model was 6th order with 28 parameters. The finally used Khosro Moghtased-Azar Department of Geodesy and Geoinformatics, Stuttgart University, Geschwister-Scholl Str. 24D, Stuttgart 70174-Germany, e-mail: Moghtased@gis.uni-stuttgart.de Piroska Zaletnyik Department of Geodesy and Surveying, Budapest University of Technology and Economics, H-1521 Budapest, P.O. Box 91, Hungary, e-mail: zaletnyikp@hotmail.com neural network model contained 7 neurons in it's one hidden layer, with radial basis activation functions, with 31 parameters. These two models, with same order of numbers of parameters, were compared. Calculating the remained errors at the training points the two models had the same fitting precision. However according to the testing point's results, the neural network model offered more reliable results, with 2-3 times smaller errors.The computations were performed with the Mathematica software, and the results are given in a symbolic form which can be used in the analysis of crustal deformation, e.g. strain analysis.
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