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Journal of Seismology - The determination of an earthquake’s magnitude correctly is vital to understand the situation and prevent loss of lives and planning first organization for the rescue... 相似文献
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Meral?BuyukyildizEmail author Gulay?Tezel 《Theoretical and Applied Climatology》2017,128(1-2):181-191
In this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-ε support vector regression (PSO-εSVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-εSVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; minR 2 = 0.9169, maxR 2 = 0.9995, medianR 2 = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations. 相似文献
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We have studied seismic surface waves of 255 shallow regional earthquakes recently recorded at GEOFON station ISP (Isparta, Turkey) and have selected these 52 recordings with high signal-to-noise ratio for further analysis. An attempt was made by the simultaneous use of the Rayleigh and Love surface wave data to interpret the planar crust and uppermost mantle velocity structure beneath the Anatolian plate using a differential least-square inversion technique. The shear-wave velocities near the surface show a gradational change from approximately 2.2 to 3.6 km s− 1 in the depth range 0–10 km. The mid-crustal depth range indicating a weakly developed low velocity zone has shear-wave velocities around 3.55 km s− 1. The Moho discontinuity characterizing the crust–mantle velocity transition appears somewhat gradual between the depth range 25–45 km. The surface waves approaching from the northern Anatolia are estimated to travel a crustal thickness of 33 km whilst those from the southwestern Anatolia and part of east Mediterranean Sea indicate a thicker crust at 37 km. The eastern Anatolia events traveled even thicker crust at 41 km. A low sub-Moho velocity is estimated at 4.27 km s− 1, although consistent with other similar studies in the region. The current velocities are considerably slower than indicated by the Preliminary Reference Earth Model (PREM) in almost all depth ranges. 相似文献
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