Analysis of Precipitation Anomaly and a Failed Prediction During the Dragon-boat Rain Period in 2023

This study investigates the possible causes for the precipitation of Guangdong during dragon-boat rain period(DBRP) in 2022 that is remarkably more than the climate state and reviews the successes and failures of the prediction in2022. Features of atmospheric circulation and sea surface temperature(SST) are analyzed based on several observational datasets for nearly 60 years from meteorological stations and the NCEP/NCAR Global Reanalysis Data. Results show that fluctuation of the 200-h Pa weste...     

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province, China. The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing. At the same time, the wind speed predicted by the EC model is also included for comparative analysis. The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A. Th...     

改进灰色马尔科夫模型在基坑预测中的研究

基坑预测问题关系到工程施工的安全,在施工过程中对基坑进行周密的监测和变性预测分析显得尤为重要。针对传统预测模型存在固有偏差和可靠性低的缺点,采用新陈代谢的原理对无偏灰色加权马尔科夫模型进行改进。该模型先用无偏灰色模型拟合系统的总体变化趋势,然后,对拟合残差进行马尔可夫状态划分,并根据各阶权重对不同步长的转移矩阵进行加权处理,用加权后的无偏灰色马尔科夫模型进行预测。在每一步的预测中,利用新陈代谢的原理不断更新建模所使用的数据。将该模型用于基坑沉降预测,并通过实例进行验证。实验表明:基于新陈代谢的无偏灰色加权马尔科夫模型提高了基坑沉降预测的精度和可靠性,预测精度与未改进模型相比提高了8.54%。     

基于遥感影像的土地动态变化监测

土地利用/覆盖变化是目前研究全球及区域环境的一个重要领域,在城镇化加速的今天,城镇的土地利用格局也发生了飞速的变化。本文通过其一研究区内的Landsat TM遥感影像进行处理,获取了2007~2016年10个时相土地利用/覆盖信息,通过不同的预测模型对监测到的数据进行处理及比较,根据相应的最优预测方法预测了2017~2019年南昌市各土地类型的数据,由此研究并探讨了南昌市土地利用/覆盖的时空格局变化。     

GIS支持下的油气多因素综合预测方法——以苏里格气田苏120区为例

苏里格气田为一致密沙岩气田,油气富集区的预测优选为气藏开发奠定基础。常规的油气富集区预测方法存在着多学科成果综合分析困难、预测过程复杂、效率低下等弊端,本文针对苏里格120区块开展了基于GIS的油气富集区多因素综合预测方法的研究。首先借助GIS的空间数据库,实现多学科成果图件的集成管理;然后基于空间定位,从多学科成果图件中提取预测多因子,构建油气多因素综合预测模型;再次,运用GIS强大的叠加分析方法进行多因素综合预测,自动生成油气富集区预测平面图。本文的研究,为油气富集区的预测提供了新的技术方法,有效地提高了预测效率。     

两种不同的SVM建模方法在大坝变形预测中的应用

用支持向量机对大坝变形监测数据建模分析和预测一般有两种方法:一是仅用大坝的变形数据作为输入端和输出端,构建支持向量机模型;二是用温度、水压等大坝变形的影响因子作为输入端,大坝变形数据作为输出端,构建支持向量机模型。两种建模方法比较研究鲜有讨论,文中用这两种建模方法对福建省某一大坝进行建模预测。结果表明,第二种方法建模预测速度更快,预测精度更高。     

空间对准关系及其定量描述方法

空间对准是最基本的空间排列形式之一,属于高层次的空间关系概念。根据Gestalt心理学的空间认知原理,提出空间对准的概念及其定量描述和计算方法。通过定义正对投影长度比,实现空间对准的定量描述与计算,表明其具有平移、旋转、尺度不变性,弱反身性,不可传递性等。通过模拟实验分析和城市双线道路识别与建筑群直线模式识别两个真实案例,证明文中方法的实用性和有效性。     

BCC二代气候系统模式的季节预测评估和可预报性分析

本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

改进的等维灰数递补模型预测边界确定方法

针对当前利用灰色预测模型进行地表沉降预测研究更多注重模型本身探讨而较少涉及预测值合理范围以及预测值时间响应函数的还原方法的情况,该文提出了一种改进的等维灰数递补模型预测边界的确定方法,采用了误差传播定律对预测值范围进行界定,并尝试利用中值近似处理代替累减生成建立预测值的时间响应函数;最后,结合工程实例给出了预测值上下边界,探讨了模型在误差允许范围内的预测步长,并对比分析了改进的等维灰数递补模型、原始的等维灰数递补模型和GM(1,1)模型。结果发现:改进的等维灰数递补模型具有较好的拟合效果与预测精度。