Artificial neural network study of observed pattern of scour depth around bridge piers

An artificial neural networks (ANN) model is developed to study the observed pattern of local scour at bridge piers using an FHWA (Federal Highway Administration) data set composed of 380 measurements at 56 bridges in 13 states. Various ANN estimates of observed pier scour depth on different choices of input variables are examined. Reducing the number of variables from 14 to 9 has negligible effect on the coefficient of determination, R², (0.73 vs. 0.72). Further sensitivity analysis indicates that pier scour depth can be estimated using only four variables: pier shape and skew, flow depth and velocity with a coefficient of determination of 0.81, suggesting that inclusion of some variables actually diminishes the quality of ANN predictions of short term observed pattern of scour. The ANN estimates indicate that flow depth and flow velocity make up 66% of the coefficient of determination.     

Identification of cryovolcanism on Titan using fuzzy cognitive maps

Future planetary exploration of Titan will require higher degrees of on-board automation, including autonomous determination of sites where the probability of significant scientific findings is the highest. In this paper, a novel Artificial Intelligence (AI) method for the identification and interpretation of sites that yield the highest potential of cryovolcanic activity is presented. We introduce the theory of fuzzy cognitive maps (FCM) as a tool for the analysis of remotely collected data in planetary exploration. A cognitive model embedded in a fuzzy logic framework is constructed via the synergistic interaction of planetary scientists and AI experts. As an application example, we show how FCM can be employed to solve the challenging problem of recognizing cryovolcanism from Synthetic Aperture Radar (SAR) Cassini data. The fuzzy cognitive map is constructed using what is currently known about cryovolcanism on Titan and relies on geological mapping performed by planetary scientists to interpret different locales as cryovolcanic in nature. The system is not conceived to replace the human scientific interpretation, but to enhance the scientists’ ability to deal with large amounts of data, and it is a first step in designing AI systems that will be able, in the future, to autonomously make decisions in situations where human analysis and interpretation is not readily available or could not be sufficiently timely. The proposed FCM is tested on Cassini radar data to show the effectiveness of the system in reaching conclusions put forward by human experts and published in the literature. Four tests are performed using the Ta SAR image (October 2004 fly-by). Two regions (i.e. Ganesa Macula and the lobate high backscattering region East of Ganesa) are interpreted by the designed FCM as exhibiting cryovolcanism in agreement with the initial interpretation of the regions by Stofan et al. (2006). Importantly, the proposed FCM is shown to be flexible and adaptive as new data and knowledge are acquired during the course of exploration. Subsequently, the FCM has been modified to include topographic information derived from SAR stereo data. With this additional information, the map concludes that Ganesa Macula is not a cryovolcanic region. In conclusion, the FCM methodology is shown to be a critical and powerful component of future autonomous robotic spacecraft (e.g., orbiter(s), balloon(s), surface/lake lander(s), rover(s)) that will be deployed for the exploration of Titan.     

基于人工神经网络的边坡抗震抗滑稳定性评判方法

从人工神经网络的基本原理出发，建立了边坡抗震抗滑稳定性评判的人工神经网络模型。选择四川和云南地区的70个边坡实例作为学习样本,对BP算法进行了学习和检验。实例计算表明,BP网络性能良好，所建立的模型预测精度高，具有一定的工程实用价值。神经网络法是一种有效可行的新方法。     

鸭河口水库流域高炮人工增雨试验效果评价

1998，1999年夏季在鸭河口水库流域进行了高炮人工增雨作业试验，结果表明，借助雷达引导，选择适当作业时机，高炮增雨作用一般可增雨20％－40％。     

山东人工增雨宏观条件分析

提要 该文以1979～1989年4～6月、9～10月全省125个站逐时降雨量资料，分析了山东降雨的时、空分布。针对作业季节飞机人工增雨特点，将759个降雨日分为14种天气形势，对其形势特点、降雨特征、云状与降雨的关系作了较详细的统计分析。指出了不同月份飞机人工增雨的作业高度，得出了增雨潜力较大的天气、时间和地区，为增雨作业提供依据。     

人工神经网络方法在地震预报中的初步应用

以陕西地区的地震为例，探讨了人工神经网络方法在地震预报中的应用。预报因子采用Ｋｅｉｌｉｓ－Ｂｏｒｏｋ提出的地震流函数。结果表明，人工神经网络方法能够较好地学习复杂的预报因子和预报对象的关系，模拟地震预报问题，预报效果也较好，有广阔的应用前景。     

河北坝上疏缓丘陵华北落叶松人工林生长特性研究

研究表明，坝上疏缓丘陵区华北落叶松（Ｌａｒｉｘｐｒｉｎｃｉｐｉｓ－ｒｕｐｐｒｅｃｈｔｉｉ）人工林高１５龄前生长阴坡大于阳坡，１５－２１龄高生长从大到小依次为阴坡中部、阳坡下部、阴坡下部；直径、材积生长阳坡大于阴坡。因此，在降水量、气温均低于华北落叶松天然分布区的坝上地区，丘陵阴坡水分条件较好，强阳性树种华北落叶松幼树生长较快，容易形成人工片林，而丘陵阳坡热量条件较好，幼树生长较慢，但成林后林分生产力较高。丘陵阴坡中部的华北落叶松人工林，２８龄后可进行天然更新，而其它立地３０龄以前不能天然更新。     

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar,Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.     

Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the resulting techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. A spatial database of the topography, soil type, timber cover, geology, and land cover was constructed and the landslide-related factors were extracted from the spatial database. Using these factors, the susceptibility to landslides was analyzed by artificial neural network methods. The results of the landslide susceptibility maps were compared and verified using known landslide locations at another area, Yongin, in Korea. A Geographic Information System (GIS) was used to analyze efficiently the vast amount of data and an artificial neural network turned out to be an effective tool to analyze the landslide susceptibility.     

人工神经网络及其在短期气候预测中的应用

从业务实际出发，提出了面向短期气候预测的神经网络。选用主分量作为网络的输入和输出，大大减少了输入输出节点数，重点突出了大尺度影响变化关系，提高了实际预报的稳定性；用全局寻优的SA算法取代经典BP算法，使网络学习训练更充分，知识获取更全面；针对业务实际设计代价函数，保证了网络学习训练能适应短期气候预测业务的要求，学习目的更明确，针对性更强。