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排序方式: 共有197条查询结果,搜索用时 15 毫秒
1.
干旱区水资源约束下的生态退耕空间优化及权衡分析——以奇台县为例 总被引:1,自引:0,他引:1
干旱区耕地大规模扩张导致水资源超载、生态问题频发,亟须退地还水。因此,探索水资源约束下的生态退耕空间优化方案,不仅有助于解决干旱区面临的问题,还可为土地利用规划和决策提供参考。目前对退耕方案多效益矛盾的研究尚量化不足,有鉴于此,本文建立生态退耕空间优化配置及权衡分析模型,以新疆奇台县为研究区进行模型应用,构建了水资源约束下的生态退耕方案群,并设计保有耕地优先和保护生态优先等两种情景,利用蚁群优化算法实现了生态退耕的空间配置模拟与优化。结果表明:耕地面积在保有耕地优先情景将减至9.94万hm2,保护生态优先情景则为6.96万hm2;在此退耕过程中,河道内生态用水占水资源总量的比例从10%增加到30%,防固沙量由713.22万t增加至816.59万t,经济效益则由34.86亿元下降至24.75亿元。通过比较单位经济效益的减少比例可产生的生态效益增加比例,确定耕地面积退至8.35万hm2时是权衡生态和经济效益下的生态退耕最优方案。退耕还草主要发生在奇台县耕地集中区的东北、西北及西南部边缘,将有利于北部的沙漠化防控和南部的水源保护。本研究实现了干旱区水资源约束下生态退耕方案空间模拟和优化,对农业经营和生态保护具有重要参考意义。 相似文献
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The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr−1, whereas the Rrs(λ) uncertainty at 412 nm is the highest (APDm 47–62%, RMS 0.0027–0.0041 sr−1). The uncertainties of the aerosol optical thickness (AOT) τa, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task. 相似文献
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From pollen percentage to vegetation cover: evaluation of the Landscape Reconstruction Algorithm in western Norway 总被引:1,自引:0,他引:1 下载免费PDF全文
KARI LOE HJELLE INGVILD KRISTINE MEHL SHINYA SUGITA GIDSKE LEKNÆS ANDERSEN 《第四纪科学杂志》2015,30(4):312-324
The Landscape Reconstruction Algorithm (LRA) with the two models REVEALS and LOVE is developed to transform pollen percentage data to vegetation cover. This paper presents the first study to evaluate LRA in a region with large topographic variations within a short distances. The REVEALS model estimates regional vegetation abundance based on pollen assemblages from large lakes (100–500 ha). Pollen surface samples from one large and 28 small lakes are used together with a combination of regionally derived pollen productivity estimates and available estimates from other regions of Europe. The results show a good relationship between REVEALS‐estimated forest cover and vegetation abundance based on the CORINE land‐cover data. The REVEALS results using various sets of pollen assemblages from small lakes were comparable to those using one large lake. Local vegetation abundance using the LOVE model was estimated around 26 lakes. For common taxa, such as Pinus and Poaceae, the LOVE‐based estimates of plant abundance match well with the distance‐weighted plant abundances based on vegetation maps. Our results indicate that the LRA approach is effective for reconstruction of long‐term vegetation changes in western Norway and other regions with high topographic relief when no major gradients exist in the pollen data. 相似文献
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Amir Semnani Liang Wang Mehdi Ostadhassan Majid Nabi-Bidhendi Babak Nadjar Araabi 《Geophysical Prospecting》2019,67(7):1701-1719
Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings. 相似文献
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使用模拟退火算法,以2019年4月6日江苏溧水ML 3.3地震为典型震例,进行地震定位反演试算,确定适用于江苏及邻区地震定位的最佳初始温度、降温策略和扰动函数。设置初始温度为25,降温策略为T = 0.96T,选用效率较高的局部收敛加强型方法产生随机模型,对2019年江苏及周边地区105次ML≥1.8地震进行重新定位,结果表明,76%的事件震中经、纬度定位误差绝对值在0.1°以内,但深度定位误差较大。鉴于该算法对P波到时信息及地震台站空间分布的依赖性,认为模拟退火算法在江苏及邻区陆地地震定位中效果较好,对于海域地震,则定位误差较大。 相似文献
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针对温度传感器测量中易受湿度影响的问题,通过对思维进化算法(Mind Evolutionary Algorithm,MEA)中的趋同操作、异化操作及收敛条件进行研究改进,对趋同操作中的散布权值进行自适应调整,在异化操作中引入差分进化算法的变异操作,并考虑收敛条件中搜索平面平缓的情况,提出了基于改进思维进化算法的BP神经网络湿度补偿方法.由湿度影响检定实验得到的样本数据,利用此补偿方法建立湿度补偿模型,将补偿结果与未经优化的BP神经网络模型的结果进行比较研究.结果表明,基于改进思维进化算法的BP神经网络模型补偿精度较高,收敛速度快,计算量小,可有效提高温度传感器的测量精度和可靠性,便于实际应用. 相似文献