Approach to Mountain Hazards in Tibet, China

Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

Infiltration at the Tai rain forest (Ivory Coast): Measurements and modelling

Infiltration experiments have been performed at three sites along a well-known catena under virgin tropical rain forest using a portable sprinkling infiltrometer. Experimentally determined infiltration curves are presented. Infiltration curves are also simulated on the basis of the Mein-Larson equation. The parameters for this model have been obtained from the infiltration curves (saturated conductivity) and simple soil moisture determinations (fillable porosity). The agreement between experimentally determined and modelled infiltration is reasonable, provided (a) saturated conductivity as derived from the experimental data is corrected, (b) a storage parameter, also derived from the experimental data, is added to the Mein-Larson model, and (c) the decline in soil porosity with depth is either small or occurs abruptly at shallow depth. Comparison of observed infiltration rates with rainfall intensity shows that Horton Overland Flow has to occur naturally at least on the middle and lower section of the catena. Despite the fact that most parameters can be estimated in principle from basic soil data, it remains advisable to obtain sprinkling infiltrometer field measurements, because of soil variability due to dynamic surface conditions, macroporosity, air entrapment, and irregularity of the wetting front.     

Comments on ‘A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass’

Singh et al (2005) examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However, the model proposed by them has some drawbacks according to fuzzy logic principles. This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.     

Roles of Biogeochemical Processes in the Oceanic Carbon Cycle Described with a Simple Coupled Physical-Biogeochemical Model

To evaluate the contribution of biogeochemical processes to the oceanic carbon cycle and to calculate the ratio of calcium carbonate to organic carbon downward export, we have incorporated biological and alkalinity pumps in the yoked high-latitude exchange/interior diffusion-advection (YOLDA) model. The biogeochemical processes are represented by four parameters. The values of the parameters are tuned so that the model can reproduce the observed phosphate and alkalinity distributions in each oceanic region. The sensitivity of the model to the biogeochemical parameters shows that biological production rates in the euphotic zone and decomposition depths of particulate matters significantly influence horizontal and vertical distributions of biogeochemical substances. The modeled vertical fluxes of particulate organic phosphorus and calcium carbonate are converted to vertical carbon fluxes by the biological pump and the alkalinity pump, respectively. The downward carbon flux from the surface layer to the deep layer in the entire region is estimated to be 3.36 PgC/yr, which consists of 2.93 PgC/yr from the biological pump and 0.43 PgC/yr from the alkalinity pump, which is consistent with previous studies. The modeled rain ratio is higher with depth and higher in the Pacific and Indian Oceans than in the Atlantic Ocean. The global rain ratio at the surface layer is calculated to be 0.14 to 0.15. This value lies between the lower and higher ends of the previous estimates, which range widely from 0.05 to 0.25. This study indicates that the rain ratio is unlikely to be higher than 0.15, at least in the surface waters.     

由粒子吸收光谱提取浮游植物吸收光谱的人工神经网络方法

基于现场实验数据集及人工神经网络技术,论文提出了一种从海中粒子吸收光谱提取浮游植物吸收光谱的方法。这个数据集包含了海中粒子吸收光谱和对应的浮游植物吸收光谱,并被分为三个子集:训练集、印证集和试验集。本研究所利用的人工神经网络系统为多层感知器,训练后的人工神经网络的性能由印证集和试验集来评价。实验结果表明,文中所提出的方法可成功地提取浮游植物的吸收光谱,其提取精度与传统的实验方法相当。     

毛蚶人工育苗技术

根据对毛蚶性腺发育、产卵孵化、浮游幼虫及稚贝习性等的研究，初步探索了其人工育苗中主要技术环节。2龄毛蚶全为雄性或雌雄同体，3龄以上群体雌性占70％以上。26．6～27．4℃，受精卵22h发育到D形幼虫；26．5～23．0℃，D形幼虫经过15天发育到眼点幼虫；25．O～20．0℃，投附着基、13天左右稚贝壳长达到600μm时，可以出池到室外进行中间育成。     

智能潜器的集成仿真系统

本文介绍了智能潜器集成仿真系统的硬、软件结构．通过这一系统所展示的水下虚拟仿真环境能够在研究和开发智能潜器的控制体系结构、潜器载体的水动力学、信息融合和目标识别等工作中发挥巨大作用