Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.     

Modeling of the radial and transverse components of the albedo function

In this work, we shall confine ourselves to solve analytically the integrals called , for the two components of albedo radiation pressure on an Earth’s satellite. When the Earth’s albedo is variable, as far as we know, this case has not been dealt with analytically. We shall solve these two integrals when the satellite’ horizon is illuminated and when the sun lies on the equator. This approach will facilitate the evaluation of the mentioned two equations. We will also compare our results with previous works obtained numerically.     

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。