Earthquake acceleration analysis using wavelet method

There are several methods for analyzing the acceleration of an earthquake.In this research,a discrete wavelet theory based on the Mallat method was employed to analyze the acceleration of earthquake records.For this purpose,first,the acceleration of the main earthquake was determined using the method of banding,filtering and correction of a filtered wave.Then,the acceleration of the earthquake up to five stages was decomposed using discrete wavelet theory.In this method,in which the Down-Sampling rule is utilized in each step,the number of earthquake record points is half past.Each of the waveforms was based on the acceleration of the maximum original earthquake,and the maximum acceleration in all the waves was identical.For each of the five waves obtained from wavelet decomposition,the velocity curve and ground acceleration are obtained and compared with each other.Finally,a structure was analyzed using the main wave of the earthquake and each of the waveforms was analyzed in five stages and their dynamic response curves were compared.The results showed that until the third stage of the wavelet decomposition,the error was insignificant and the dynamic response to the magnitude of the earthquake was small.The analysis time is about 10% of the analysis time with the main wave,and the error is less than 6%.     

Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland,Iran

One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.