Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

基于地理本体的空间地址校验

本文针对空间地址校验问题,构建地理本体模型,并在模型基础上设计空间计算的相关算法,进行空间地址校验和推理。在原型系统中,以Prot啨g啨为平台,使用OWL构建本体模型,SWRL描述推理规则,通过JESS推理引擎,形成新的知识,实现了POI的空间地址校验,对地理本体的研究进行了探索。     

Comparing the Kalman filter with a Monte Carlo-based artificial neural network in the INS/GPS vector gravimetric system

Rigorous physical and mathematical analysis has been intensively developed to obtain the gravity disturbance vector from the inertial navigation system and the global positioning system. However, the combination of the observation noise and the systematic INS errors make it very challenging to accurately and efficiently describe the dynamics of the system with rigorous equations. Thus, the accuracy of the gravity disturbance estimates, especially in the horizontal components, is limited by the insufficient error models. To overcome the difficulty of directly modeling the systematic errors with exact mathematical equations, a Monte Carlo based artificial neural network is successfully applied in the moving base gravimetric system. The computation results show significant improvement in the precision of all components of the gravity disturbance estimates.     

融合物理理解与模糊逻辑的分类强对流客观短期预报系统:（2）表现评估

本文对分类强对流客观短期概率预报系统2022年6月13日强对流过程预报产品的表现进行分析,基于2022年的雷暴、短时强降水、雷暴大风及冰雹客观概率预报产品和可用的分类强对流监测实况资料,结合强对流预报业务中使用的空间检验方法和常用的确定性及概率性检验指标,对该短期预报系统提供的四类强对流天气客观概率预报产品进行了详细的性能评估。用于评估的预报资料是时段为2022年4月1日至9月30日每天08时(北京时)起报,96 h内逐12 h间隔的预报产品。预报个例分析显示,四类产品均可提前24 h指示需要关注的强对流天气区域。统计检验结果表明,短时强降水各方面性能最好,其次是雷暴,雷暴大风也有一定的可参考性。四类强对流天气预报产品均存在预报概率与实况频率相比偏高的过度预报问题。雷暴、短时强降水和雷暴大风预报产品均存在与预报覆盖时效有关的日变化。评估结果为预报模型和系统后续改进发展奠定了基础,为应用基于融合物理理解与模糊逻辑人工智能方法的分类强对流预报产品提供了有益参考。     

中国对虾人工诱导雌核发育的研究Ⅰ──四步诱导法

１９９０～１９９３年于青岛采用研究鱼类人工诱导雌核发育相类似的方法 ──紫外线照射精子、受精、温度或细胞松胞素Ｂ处理卵子方法，对中国对虾进行 人工雌核发育的研究。实验结果，有三批材料获得较好的效果：二批培育到蚤状幼 体，另一批培育到３～３．５ｃｍ幼虾。经受精细胞学观察，被照射的精子经受精，其核 有的不能原核化，有的虽能原核化，且能向中央迁移，但都不能与卵原核结合。实验 表明，这三批育出的个体是雌核发育的个体。     

秋茄（Kandelia obovata）和无瓣海桑（Sonneratia apetala）红树人工林消波效应量化研究

采用野外监测的方法对海南东寨港三江湾秋茄和无瓣海桑红树人工林的消波效应进行了量化研究。结果表明,裸露海滩的消波作用较弱,波浪在经过裸滩50m后,1/10波高、1/3波高和平均波高分别减低2.9%、4.4%和5.8%。红树林的消波作用显著,总体来说,波浪在经过秋茄林50m后,1/10波高、1/3波高和平均波高分别减低46...     

青岛近岸能见度逐小时分级预报模型初探

本文对青岛2014~2015年4~8月份位于青岛奥帆基地站和胶州湾跨海大桥上的大桥4站的逐小时自动气象站测量数据进行多种统计分析工作,发现两地能见度变化影响因子有不同特点,大桥4站在雾出现时降温增湿过程更明显。能见度处于1km以下时,风速降低,温度降低,绝对湿度减小会导致更低的能见度出现。使用神经网络方法分别对两站建立逐小时的能见度分级预报模型,并用2016年4~8月数据进行预报检验,表明少数关键预报因子就可以较好地建立预报模型,奥帆基地站能见度0~1km和1~5km级别的TS评分可超过0.4,因子增多会提升能见度1~5km级别的预报效果。两站独立建模比使用一个模型预报效果更好。该研究为青岛近岸能见度精细化客观定量预报提供参考。     

Advances of geo-spatial intelligence at LIESMARS

ABSTRACT

The enhancement of computing power, the maturity of learning algorithms, and the richness of application scenarios make Artificial Intelligence (AI) solution increasingly attractive when solving Geo-spatial Information Science (GSIS) problems. These include image matching, image target detection, change detection, image retrieval, and for generating data models of various types. This paper discusses the connection and synthesis between AI and GSIS in block adjustment, image search and discovery in big databases, automatic change detection, and detection of abnormalities, demonstrating that AI can integrate GSIS. Moreover, the concept of Earth Observation Brain and Smart Geo-spatial Service (SGSS) is introduced in the end, and it is expected to promote the development of GSIS into broadening applications.     

Introducing run-size driven fisheries management for the coastal fishery of Atlantic salmon: Preseason forecasts for policy makers

The Finnish coastal fishery of Atlantic salmon (Salmo salar) in the northern Baltic Sea is regulated using multi-annual, pre-fixed, opening dates of harvests that aim to enhance spawning escapement of early migrating wild salmon. Such an inflexible management regime does not set regulations that track varying run sizes of salmon. We introduce an array of computational intelligence techniques to estimate and forecast coastal run size and escapement of salmon into three spawning rivers in the northern Baltic Sea. Our results indicated that the present management pattern, driven largely by regional fisheries policy, contrasts greatly with a “run-size driven” (i.e. abundance-based) management approach. Introducing run-size driven management, i.e. setting regulations annually by tracking preseason forecasts, would better ensure adequate escapement and at the same time allow the maintenance of coastal catch at sustainable level. Setting regulations annually would allow effective harvesting in years when the run is high, and would effectively restrict harvests when the run is low.     

地理信息科学发展与技术应用

本文回顾了中国科学院地理科学与资源研究所在地理信息科学研究与技术应用方面的历史过程,从早期的测量和制图的研究,到开创中国地理信息学科,建立资源与环境信息系统国家重点实验室的历史,是中国特色原创地理信息理论发展的历史,是中国具备自主研发世界级地理信息软件的历史,是地理信息为国家重大战略提供坚实科技支撑的历史。本文主要从地图学、地学遥感、地理信息科学、地学数据共享、重大技术突破和国家战略支撑等方面进行概述,最后从地学知识图谱、地理大数据分析、遥感人工智能、地理系统模拟和知识服务角度展望地理科学发展的新科学范式。