SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK     

WANGXie-kang HUANGEr CUIPeng 《中国地理科学(英文版)》2003,13(3):262-266

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.  相似文献   

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

Ashu Jain  K. P. Sudheer  Sanaga Srinivasulu 《水文研究》2004,18(3):571-581

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.  相似文献   

形变大地测量学的进展、问题与地震预报   总被引：12，自引：7，他引：5  

周硕愚  吴云  李正媛  杜瑞林 《大地测量与地球动力学》2004,24(4):95-101

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。  相似文献   

三峡库首区重点断裂围限区蓄水的加卸载效应     

陈蜀俊  姚运生  蔡永建 《大地测量与地球动力学》2006,26(4):97-101

通过对蓄水前后三峡库首区构造应力场进行三维数值模拟发现,相对于蓄水前,蓄水至135 m水位时,官渡口断裂-马鹿池断裂-牛口断裂围限区、秭归地理中心和九湾溪断裂带,出现了4个主应力下降区和共轭的主应力增长区,形成了差应力场;175 m水位与135 m水位相比,局部差应力场呈现逆向变化,即形成了加载效应与卸载效应的交替过程。这一过程,可能是水库诱发地震的诱震(或触震)因素。  相似文献   

Source mechanism of mining induced seismic events — Resolution of double couple and non double couple models     

Jan &#x;ílený  Alexander Milev 《Tectonophysics》2008,456(1-2):3

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

Tarkan Erdik  Zekai Şen 《Journal of Earth System Science》2008,117(6):973-974

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.  相似文献   

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     

刘雪锋  张亭禄 《海洋技术学报》2006,25(3):45-50

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。  相似文献   

毛蚶人工育苗技术   总被引：9，自引：0，他引：9  

刘爱英  马云聪  赵光环  吕振波  徐宗法  王育红 《海洋湖沼通报》2005,(1):86-90

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。  相似文献   

智能潜器的集成仿真系统     

庞永杰  徐玉如  尚游  彭良 《海洋工程》1997,(4)

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  相似文献   

赤点石斑鱼人工配合饵料中蛋白质最适含量的研究   总被引：11，自引：1，他引：11  

陈学豪  林利民  洪惠馨 《台湾海峡》1995,(4)

本文探讨了不同蛋白质含量的配合饵料对赤点石斑鱼（Epinephelusakaara）生长的影响。通过60d度验得出：在各配合饵料组中，蛋白质含量为49．52％的试验的相对增重率（46．84％）体长相对增长率（15．96％）、蛋白质效率（60．35％）和饵料转化率（29．85％）等均为最佳。配合饵料组鱼体肌肉营养成分（蛋白质、脂肪、氨基酸和鲜味氨基酸）也均优于对照组，而更接近野生鱼。经回归方程分析，赤点石斑鱼人工配合饵料中蛋白质最适含量为48．37％～49．24％。  相似文献