Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation. 相似文献
Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples. 相似文献
Izvestiya, Atmospheric and Oceanic Physics - The population of the Amur tiger (Panthera tigris altaica) in the Russian Far East is currently being restored; young individuals are being spread... 相似文献
Gravity retaining wall with geogrids has showed excellent seismic performance from Wenchuan great earthquake. However, seismic damage mechanism of this kind of wall is not sufficiently clear. In view of this, a large shaking table test of the gravity retaining wall with geogrids to reinforce the subgrade slope was carried out, and based on the Hilbert-Huang transform and the marginal spectrum theory, the energy identification method of the slope dynamic failure mode was studied. The results show that the geogrids can effectively reduce displacement and rotation of the retaining wall, and it can effectively absorb the energy of the ground movement when combined with the surrounding soil. In addition, it also reveals the failure development of the gravity retaining wall with geogrids to reinforce the subgrade slope. The damage started in the deep zone near the geogrids, and then gradually extended to the surface of the subgrade slope and other zones, finally formed a continuous failure surface along the geogrids. The analysis results of the failure mode identified by the Hilbert marginal spectrum are in good consistency with the experimental results, which prove that the Hilbert marginal spectrum can be applied to obtain the seismic damage mechanism of slope.