Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.     

Do benthic biofilters contribute to sustainability and restoration of the benthic environment impacted by offshore cage finfish aquaculture?

Benthic biofilters were deployed under a cage fish farm and in two reference locations to assess the influence of the farm on the biofilters and the surroundings, as well as to verify the usefulness of this technology as a mitigation tool. The biofilters underneath the farm recruited a fouling community practically identical to that of the control biofilters, which included a variety of trophic strategies. The former showed a higher ¹⁵N enrichment, indicating that fouling beneath the farm was benefiting from the farm waste. The waste retention efficiency was low (0.02 g N m⁻² month⁻¹) beneath the farm. Benthic biofilters aggregated demersal wild fish around and within them. Pelagic wild fish also frequently used the biofilters beneath the farm, forming compact shoals around them. The increased complexity of the habitat below the fish farm enhanced biodiversity, but this improvement did not lead to the recovery of the sediments around the biofilters.     

基于MATLAB神经网络方法的多层砖房震害预测

提出利用MATLAB人工神经网络工具箱建立基于贝叶斯正则算法的BP神经网络模型,以地震区多层砖房震害调查数据为因子的震害预测方法.神经网络模型输入震害因子包括建筑的层数、施工质量、房屋整体性等,输出值为建筑物在地震作用下的破坏程度.结果表明,本方法可以对多层砖房的震害样本进行预测并达到较理想的效果.     

聊古1井在不同取水模式下气体观测效果的对比分析

为解决聊古1井断流问题,聊城地震水化试验站先后引进、开发了人工激发引流观测技术和潜水泵变频稳流抽水技术.对天然自流观测系统、人工激发引流观测系统和潜水泵变频稳流抽水观测系统等3种不同取水模式下产出的气体观测数据进行一致性分析.结果表明,人工激发引流观测系统下气体观测动态特征年变比较明显,潜水泵变频稳流抽水观测系统下气体观测动态特征与天然自流状态下的动态特征基本一致.     

Artificial neural network forward modelling and inversion of electrokinetic logging data

An artificial neural network method is proposed as a computationally economic alternative to numerical simulation by the Biot theory for predicting borehole seismoelectric measurements given a set of formation properties. Borehole seismoelectric measurements are simulated using a finite element forward model, which solves the Biot equations together with an equation for the streaming potential. The results show that the neural network method successfully predicts the streaming potentials at each detector, even when the input pressures are contaminated with 10% Gaussian noise. A fast inversion methodology is subsequently developed in order to predict subsurface material properties such as porosity and permeability from streaming potential measurements. The predicted permeability and porosity results indicate that the method predictions are more accurate for the permeability predictions, with the inverted permeabilities being in excellent agreement with the actual permeabilities. This approach was finally verified by using data from a field experiment. The predicted permeability results seem to predict the basic trends in permeabilities from a packer test. As expected from synthetic results, the predicted porosity is less accurate. Investigations are also carried out to predict the zeta potential. The predicted zeta potentials are in agreement with values obtained through experimental self potential measurements.     

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.     

神经网络敏感性分析及其在遥感影像分类中的应用

针对基于连接权的神经网络敏感性分析方法中求取敏感性系数的不稳定性,提出一种优化连接权的神经网络敏感性分析方法。首先采用遗传算法根据误差最小化原则对神经网络进行优化,在优化的神经网络模型上进行基于连接权的敏感性分析。以1个数值模拟实例和华盛顿广场地区的遥感图像地物分类为例,验证所提方法的有效性。实验结果表明,所提方法求取输入变量的敏感性系数是稳定有效的,能有效筛选出遥感图像中对分类贡献较大的特征波段,达到降维的同时提高分类精度。     

控制水稻分蘖角度对群体生态特性的影响

控制分蘖角度对群体温度、群体相对湿度、群体CO2浓度和光合有效辐射均会产生一定影响。选用2006年水稻有关研究数据,分析水稻分蘖角度对群体生态特征的影响。结果表明：各生育时期群体温度在07:00－19:00处理大于CK。分蘖高峰期到孕穗期CK白天群体相对湿度大于处理,齐穗期到灌浆期处理群体相对湿度大于CK。群体CO2浓度在拔节期和孕穗期均为处理大于CK,其他生育期差异不显著。光合有效辐射垂直分布是处理前期和后期上层截获的光能均小于CK,群体内消光系数小。控制分蘖角度形成了有较高温度、较低湿度、高CO2浓度和适宜光分布的群体,可为获得高产奠定基础。     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.