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Ocean Dynamics - Some efforts have been done by previous researchers and scientists to represent the spectral behavior of wind waves in the Persian Gulf by analyzing real-time data, but in most of... 相似文献
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Maryam Imani 《Journal of the Indian Society of Remote Sensing》2018,46(9):1389-1397
An anomaly detection method with a clustering based feature reduction is proposed in this paper to improve the performance of the Local RX detector. Because of high dimensionality of hyperspectral image and the low number of available samples in each local region around each testing pixel, the estimate of local covariance matrix is not possible. So, because of singularity problem, Local RX cannot use the local covariance matrix and misses the local structures of data to model the background clutter. To deal with this problem, a supervised clustering based feature reduction is introduced for extraction of background features with minimum overlap and redundant information. In the projected feature space with reduced dimensionality, the local structures of background pixels are estimated to efficiently model the background data. The experiments done on both synthetic and real hyperspectral images show the superior detection performance of the proposed method with a relatively high speed. 相似文献
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M. Imani R.-J. You C.-Y. Kuo 《International Journal of Environmental Science and Technology》2014,11(4):1035-1042
The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. This study focuses on developing and validating a neural methodology applicable to the short-term forecast of the Caspian Sea level. The input and output data sets used contain two time series obtained from Topex/Poseidon and Jason-1 satellite altimetry missions from 1993 to 2008. The forecast is performed by multilayer perceptron network, radial basis function, and generalized regression neural networks. Several tests of different artificial neural network (ANN) architectures and learning algorithms are carried out as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The results derived from the ANN are compared with observed sea level values and with the forecasts calculated by a routine autoregressive moving average (ARMA) model. Different ANNs satisfactorily provide reliable results for the short-term prediction of Caspian Sea level anomalies. The root mean square errors of the differences between observations and predictions from artificial intelligence approaches can be significantly reduced by about 50 % compared with ARMA techniques. 相似文献
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Water Resources - The lack of long term observed data is the main challenge in many simulation-based studies for identification of nutrient critical source areas (CSAs). This study explored the... 相似文献
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In this study, we successfully present the analysis and forecasting of Caspian Sea level pattern anomalies based on about 15 years of Topex/Poseidon and Jason-1 altimetry data covering 1993–2008, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. Since these altimetric measurements comprise of a large datasets and then are complicated to be used for our purposes, principal component analysis is adopted to reduce the complexity of large time series data analysis. Furthermore, autoregressive integrated moving average (ARIMA) model is applied for further analyzing and forecasting the time series. The ARIMA model is herein applied to the 1993–2006 time series of first principal component scores (sPC1). Subsequently, the remaining data acquired from sPC1 is used for verification of the model prediction results. According to our analysis, ARIMA (1,1,0)(0,1,1) model has been found as optimal representative model capable of predicting pattern of Caspian Sea level anomalies reasonably. The analysis of the time series derived by sPC1 reveals the evolution of Caspian Sea level pattern can be subdivided into five different phases with dissimilar rates of rise and fall for a 15-year time span. 相似文献
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