Forecast of storm surge by means of artificial neural network

This study describes the construction and verification of a model of sea level changes during a storm surge, applying artificial neural network (ANN) methodology in hydrological forecasting in a tideless sea where the variation of water level is only wind generated. Some neural networks were tested to create the forecast model. The results of ANN were compared with observed sea-level values, and with the forecasts calculated by different routine methods. The results of verification show that the neural network methodology could be successfully applied in the routine, operational forecast service.     

Application of artificial intelligence techniques in automatic hull form generation

The paper describes a methodology for automatic hull form generation of ships with some desired performances using artificial intelligence techniques. The whole implementation process is divided into three main components. First of all the half-breadth weight matrices are generated that would provide population with pre-fitness by examining the relationship of the hull form with the principal dimensions of a number of existing vessels. Secondly, breadth and draft are adjusted using Neural Network Concepts. Breadth, draft, length, displacement and speed of the ship are very related terms and relations among them create some constraints. Neural Networks solve these constraints and adjust the parameters. Finally, Genetic Algorithm is used for searching the exact solution by examining several generations. For this, the algorithms need to measure fitness for every population in every generation. Unfortunately, GA doesn't guarantee fairness of the surface of the hull form, which can't be ignored. So, for every population especially for newborn population fairing techniques would be used. However, being complex geometric shape, hull form surface can't be faired using least-square or other single equation fitting techniques. Fairness is done taking pair-wise points using B-spline functions [CAD 20(1988)].     

Artificial neural networks and their application to assessment of ultimate strength of plates with pitting corrosion

The potential for the structural capability degrading effects of both corrosion and fatigue induced cracks are of profound importance and must be both fully understood and reflected in vessel's inspection and maintenance programme. Corrosion has been studied and quantified by many researchers, however its effect on structural integrity is still subject to uncertainty, particularly with regards to localized corrosion. The present study is focused on assessing the effects of localized pitting corrosion on the ultimate strength of unstiffened plates. Over 265 non-linear finite-element analyses of panels with various locations and sizes of pitting corrosion have been carried out. The results indicate that the length, breadth and depth of pit corrosion have weakening effects on the ultimate strength of the plates while plate slenderness has only marginal effect on strength reduction. Transverse location of pit corrosion is also an important factor determining the amount of strength reduction. When corrosion spreads transversely on both edges, it has the most deteriorating effect on strength. In addition, artificial neural network (ANN) method is applied to derive a formula to predict ultimate strength reduction of locally corroded plates. It is found out that the proposed formulae can accurately predict the ultimate strength of locally corroded plates under uniaxial in-plane compression.     

湖南一次重大森林火灾人工增雨作业条件与效果分析

人工增雨作业是森林灭火的一个重要科技手段,作业条件预报与作业时机的把握是作业成败的关键。本文以湖南省“20130810”大乘山重大森林火灾人工增雨作业情况为基础,利用多种气象资料对作业条件预报与作业方案设计进行分析,采用作业前后雷达回波参量变化、最大相关系数的雷达回波跟踪法（TREC）对作业效果进行评估。结果表明：大乘山重大森林火灾人工增雨作业通过捕捉台风外围边缘快速生消的积云云系,有效地让局地积云降水降落到了位置相对集中的火场,8月15日第7、第8次地面人工增雨作业15 min后雨势加大,有效压制火势,及时解救了火场被困人员;8月16日第11次作业为明火扑灭发挥了重要作用。针对局地积云开展森林灭火人工增雨作业,使用火箭、高炮等地面装备更容易抓住作业时机,针对大范围的积层混合云系更适合开展飞机增雨作业,飞机在火情侦察拍照和气象探测等方面能发挥重要作用。     

Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system

This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.     

高等学校《地球科学导论》课程教学改革与探索

在新技术、新方法不断涌现和学科融合发展的背景下,地球科学类课程教学在如何适应未来地球系统科学发展和人才培养需要方面,正面临前所未有的挑战和机遇。论文以浙江大学《地球科学导论》课程为例,对教学内容拓展、课程思政融入和课程教学现代化面临的挑战和应对策略做了介绍,并对未来学科发展和课程教学改革方向进行了思考和展望。分析指出,课程教学改革要以立德树人、铸魂育人为根本,夯实地球科学基础,拥抱新技术新方法,并面向地球系统科学的未来。     

综述与展望：地理空间数据的管理、多尺度变换与表达

多尺度表达是地理空间数据的重要研究内容之一。本文从地理空间数据管理、地理空间数据尺度变换以及地图多尺度表达3个方面对地理空间数据的多尺度表达研究现状进行归纳和总结,对研究成果进行系统分析与展望。主要分析包括：① 在地理空间数据管理的多尺度数据库与多尺度空间索引方面,多库多版本、一库多版本和一库一版本数据库能够帮助多尺度表达方法构建较好的数据支撑,层次化的多尺度空间索引也是主流的多尺度数据库构建结构。但目前多尺度数据库与多尺度空间索引方法还不具备解决不同层次数据的集成与匹配能力,对不同尺度数据进行实时一致性调整的能力不足;② 在地理空间数据多尺度变换方面,地图自动综合能够较好地与人工智能技术相结合,但由于知识获取的限制,距离实现完全自动综合仍有一段距离;且当前智能化的自动综合研究相关成果主要用于辅助决策,对综合知识的自主学习有待进一步研究;目前多数研究是基于离散的尺度变换模式,对连续尺度变换能力不足;且缺乏强有力的质量控制机制,自动尺度变换结果存在较大的不确定性;③ 在地图多尺度表达方面,地图数据类型多源、种类丰富且使用灵活,多尺度显示的复杂性较高,当前地图可视化对地理信息中隐藏的现象与规律有待进一步挖掘。最后,从智能化自动综合方法、连续多尺度表达模型、深度学习与制图综合及“新”时代多尺度表达等方面对未来发展进行了展望。     

基于人工智能的川东北三叠系杂卤石地震识别

作为钾肥的主要来源之一,杂卤石矿藏具有旺盛的需求。我国川东北地区三叠系海相地层中,杂卤石矿藏发育。但该地区杂卤石矿体埋藏较深、分布散乱,勘探难度极大。为降低开发成本,需要充分借助地震资料的优势,为钾盐储层的描述提供钻前支持。针对该地区杂卤石层地震反射特征复杂、常规波阻抗反演方法难以直接预测的问题,本文引入人工智能算法对地震资料进行数据挖掘。本方法首先通过模糊粗糙集属性约简,提取适合富钾地层表征的敏感地震属性;然后以极限学习机为数据挖掘工具,自主学习敏感地震属性和井中实测钾含量曲线的非线性映射关系,形成了基于极限学习机的杂卤石预测方法。应用结果证明:利用极限学习机人工智能方法成功地对川东北地区的杂卤石进行了预测,取得了预期的地质效果,为该地区利用地震数据探寻杂卤石提供了一条新的勘探思路。     

生产数据的整合与初步分析在钻井中的应用实例

钻井过程中的生产数据是推动产业发展的重要驱动力,也是未来人工智能在钻井行业应用的基础。当前国内外行业巨头均已开始建立生产数据的收集与分析平台,但普通生产一线作业的数据整合与分析仍未引起重视。本文以采集自南海某区域10口井44种不同参数共21912条数据为例展现了生产数据从采集至定量分析的全流程。通过图像点采算法与数据插值补齐算法,实现不同格式数据的矩阵化整合。经过标准化与可视化的处理,可完成对整合数据的定性分析,明确生产数据的规律与趋势。在此基础上,通过统计分析、相关性分析与因子分析,可获得数据特征值,也能明确不同数据间的相互关系。分析结论实现了数据的分组与降维,在保证后续数据建模、人工智能等分析精度的同时降低了建模复杂度。     

Clogging mechanisms and preventive measures in artificial recharge systems

Groundwater which occurs in fractured rock or porous aquifers or other geological weak zones such as faults and fractures is usually extracted via boreholes, hand wells or other sources such as springs.Water scarcity has become a severe problem due to many factors, such as an alarming increase in population and per capita water consumption, over exploitation of groundwater resources and abrupt global climatic change along with its related eco-environmental geological problems. In such situation, application of artificial recharge systems(e.g. surface recharge basin and deep injection well systems) can help to effectively manage and augment the unitization of groundwater resources. However, the clogging problem,which may be caused by a complex interdependent mechanisms of physical, chemical and biological has been a challenge for the efficacy and the implementation of recharge facilities. Clogging can reduce the permeability, recharge rate and longevity of recharge facilities and increase the operational and maintenance costs. Major influencing factors associated with the occurrence of clogging include the chemical composition of groundwater(both the recharge water and native groundwater), aquifer medium and microbial diversity, together with other environmental factors such as temperature, pressure, total dissolved solids, total soluble salts, pH, Eh, nutrients, gases, carbonates and others; these factors ultimately increase the piezometric head but reduce the permeability and infiltration rates of porous/seepage media.Pretreatment of recharge water can minimize the potential clogging. In the case of clogged wells,rehabilitation methods need to be deployed. In the meantime, there is an urgent needs to understand the basic causes and developmental processes/mechanisms of clogging in order to mitigate this problem. This paper reviews the major clogging mechanisms and their possible preventive measures and redevelopments in artificial recharge systems.