The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to automatically discover relationships among nonlinear systems. Based on genetic algorithms (GAs), the relationships between input and output can be expressed as parse trees. The GAOT method typically has the disadvantages of premature convergence, which means it cannot produce satisfying solutions and performs satisfactorily when applied to only low-dimensional problems. Therefore, the GAOT method is enhanced using an automatic incremental procedure to improve the search ability of the method and avoid trapping in a local optimum. In this case study, an IGAOT is used to determine the relationship between the in situ data on the salinity of the Taiwan Strait and the data on the spectral parameters, seven wavebands, of a Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR1 and LR2) models, exhibits higher correlation coefficients, and involves fewer estimating errors. The results of this study indicate that the proposed technique is useful for estimating the Taiwan Strait salinity. 相似文献
Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies. 相似文献
Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making. 相似文献
Maximum and minimum void ratios (emax and emin) of granular soils are commonly used as indicators of many engineering properties. However, few methods, apart from laboratory tests, are available to provide a rapid estimation of both emax and emin. In this study, we present a theoretical model to map the densest and the loosest packing configurations of granular soils onto the void space. A corresponding numerical procedure that can predict both emax and emin of granular soils with arbitrary grain size distributions is proposed. The capacity of the proposed method is evaluated by predicting the maximum and minimum void ratios of medium to fine mixed graded sands with different contents of fines. The influence of the grain size distribution, characterized quantitatively by uniformity parameter and the fractal dimension, on emax and emin is discussed using the proposed method. Moreover, application of this method in understanding the controlling mechanism for the void ratio change during grain crushing is presented.