Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

Freely floating-body simulation by a 2D fully nonlinear numerical wave tank

Nonlinear interactions between large waves and freely floating bodies are investigated by a 2D fully nonlinear numerical wave tank (NWT). The fully nonlinear 2D NWT is developed based on the potential theory, MEL/material-node time-marching approach, and boundary element method (BEM). A robust and stable 4th-order Runge–Kutta fully updated time-integration scheme is used with regriding (every time step) and smoothing (every five steps). A special φ_n-η type numerical beach on the free surface is developed to minimize wave reflection from end-wall and wave maker. The acceleration-potential formulation and direct mode-decomposition method are used for calculating the time derivative of velocity potential. The indirect mode-decomposition method is also independently developed for cross-checking. The present fully nonlinear simulations for a 2D freely floating barge are compared with the corresponding linear results, Nojiri and Murayama’s (Trans. West-Jpn. Soc. Nav. Archit. 51 (1975)) experimental results, and Tanizawa and Minami’s (Abstract for the 6th Symposium on Nonlinear and Free-surface Flow, 1998) fully nonlinear simulation results. It is shown that the fully nonlinear results converge to the corresponding linear results as incident wave heights decrease. A noticeable discrepancy between linear and fully nonlinear simulations is observed near the resonance area, where the second and third harmonic sway forces are even bigger than the first harmonic component causing highly nonlinear features in sway time series. The surprisingly large second harmonic heave forces in short waves are also successfully reproduced. The fully updated time-marching scheme is found to be much more robust than the frozen-coefficient method in fully nonlinear simulations with floating bodies. To compare the role of free-surface and body-surface nonlinearities, the body-nonlinear-only case with linearized free-surface condition was separately developed and simulated.     

Spatial heterogeneity of epibenthos on artificial reefs: fouling communities in the early stages of colonization on an East Australian shipwreck

Artificial reefs are spatially complex habitats and serve as good model systems to study patterns of community succession and the response of epibiota to environmental clines over small spatial scales. Here, we quantified spatial heterogeneity in community composition and diversity of fouling communities across a number of environmental gradients that included water depth, surface orientation of habitats, exposure to currents, and shelter. Assemblage structure was quantified by spatially replicated photo transects on a recently scuttled large navy ship off the East Australian coast, lying in 27 m of water. A rich assemblage of epifauna had colonized the wreck within a year, dominated by barnacles, sponges and bryozoans. Community structure varied significantly over small spatial scales of meters to tens of meters. Depth, surface orientation and exposure were the major environmental drivers. Assemblages were substantially less diverse and abundant on the deepest (23 m near the seafloor) part of the hull with residual antifouling paint, on sheltered surfaces inside the wreck, and on the sediment‐laden horizontal surfaces. Overall, the wrecks’ habitat complexity corresponds with small‐scale heterogeneity in the fouling communities. This study supports the notion that wrecks enhance local diversity and biomass within the habitat mosaic of their location, and habitat complexity may be an important mechanism for this, as demonstrated by the large spatial variability in the assemblages documented here.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Application of visual,statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait

The ability of artificial neural network to differentiate water samples from the two aquifers of Kuwait on the basis of their major ion chemistry has been demonstrated. The major ion concentration distribution in the groundwater of the Kuwait Group and the Dammam Formation aquifers of Kuwait appears very similar. Cross-plots, supported by the discriminant function analysis of the data, however, suggest that there are some subtle differences in the overall composition of the water from the two aquifers that make it possible to differentiate the water from the two aquifers in almost 80% of the cases. An artificial neural network improved the differentiation capability to 90% of the cases. It is also possible to estimate the fraction of Kuwait Group water in the flow stream of dually completed wells with the help of an artificial neural network developed for this purpose. Electronic Publication     

福建省干旱概况及夏旱期间人工增雨条件分析

对福建省干旱的成因和干旱的环流形势进行了探讨,着重分析了夏旱期间人工增雨作业的天气形势、云状、回波特征及云顶温度等条件,结果表明福建省干旱的形成与大型环流形势、地形、地貌及土壤植被条件有关;平均而言,闽东南沿海干旱多于内陆地区;西太平洋副热带高压是夏季致旱的主要天气系统;夏季,西风槽前型、台风外围影响型、副热带辐合带型是进行人工增雨作业的有利天气形势,Sc、Cb、Cu云都有一定的人工增雨作业条件,其中降水时长1～5 h,过程雨量1.0～5.0 mm的云是旱季作业比较适合的作业云.这些结果为开展人工增雨作业提供理论依据,达到解除或缓解干旱的目的.     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

震后震型早期判定的人工神经网络方法

应用人工神经网络的方法，利用３０次强震震后１天和２天内的地震资料作为学习样本，对广西及其邻区发生的４次地震的震型作了早期预测判定，结果表明应用效果较好，正确率达７５％。该方法值得进一步研究。     

乌兰布和沙漠东北边缘植物群落物种多样性及其生态环境

应用群落多样性指数（Ｄ）、均匀度（Ｊ）、生态优势度（Ｃ）等指标，对乌兰布和沙漠东北边缘不同生境（荒漠区、绿洲外围、绿洲内）的天然植物群落进行了研究，结果表明这三种指标值能有效地表征群落的组成结构特征及生境的优劣；荒漠植物群落多样性指数（Ｄ＝０．６０３～２．９８４）和均匀度（Ｊ＝０．３３７～０．６９１）都较低，而生态优势度（Ｃ＝０．７２４～０．１９５）较高，说明荒漠植被生态环境的脆弱性，群落结构越复杂，多样性指数越高；多样性与生态环境关系密切，Ｄ和Ｊ随生境渐好而递增，Ｃ随生境渐好而递减，可作为环境评价指标；多样性在一定程度上体现了群落的发展阶段和稳定程度，越接近顶级阶段，多样性越高，且环境的改善有助于群落演替进程的加速；干旱区弃耕撩荒地次生演替植被，在弃耕初期多样性随弃耕年限增大而减小，后期处于一个较稳定的水平，且各演替阶段植物组成变化较大。     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.