Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing

Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can’t meet demand of the accurate,non-destructive and efficient monitoring and warning.Currently,remote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information,and is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions,including classification of different stresses,severity estimation and stress forecasting,are summarized,and the methods of feature extraction,feature selection,and algorithms are expounded.Then,the application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis,the existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.     

Short term solar insolation prediction: P-ELM approach

Abstract

The accurate forecasting of solar irradiance with hybrid machine learning algorithm is presented in this paper. A novel Persistence-Extreme Learning Machine (P-ELM) algorithm is used for training of the system. The Clearness Index (CI) value of the 22 districts of Andhra Pradesh (India) is calculated and out of which four areas are identified with highest CI values. The Global Horizontal Irradiance (GHI) is forecasted for the selected areas with different weather conditions such as winter, summer and rainfall seasons using a P-ELM algorithm. The input parameters are Temperature, Diffuse horizontal irradiance, pressure and past GHI and GHI for the next instant as the output is considered. The real time data is obtained for every one hour interval for a period of one month. The performance of the P-ELM algorithm is evaluated in terms of Mean Absolute Error and Root Mean Square Error. From the obtained results, it is observed that P-ELM algorithm offers better performance over the fundamental P-ELMs. The P-ELM algorithm gives good forecasting accuracy with minimum simulation time. The simulation of P-ELM algorithm is carried out using MATLAB 2013a environment. The P-ELM algorithm is very much beneficial for accurate and reliable real time solar forecasting.     

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision‐making and process‐quality measurements.     

智能组合预测法在短期电力负荷预测中的应用研究

提出将Kohonen网络、Elman神经网络和遗传算法结合起来建立一种智能组合预测模型,此模型能够综合各种单一预测模型的优点,内在结构随时间的推移不断变化,符合电力负荷的特点,提高了负荷预测的精度.文中给出了三种网络模型进行短期电力负荷预测的仿真结果比较,从而验证了智能组合预测模型的合理性和良好的应用前景.     

Investigations of groundwater bursting into coal mine seam floors from fault zones

This paper presents a case study of investigations into retarded groundwater bursting along the fault zones in the seam floors in coal mines. In addition to in situ measurements of ground stresses, a number of rock samples from the fault zones at the site of the case study were taken, and laboratory tests were performed for conventional and special rheological rock mechanics properties. The effects of different parameters on deformation and failure of the fault zone materials were analyzed, and the mechanism of retarded groundwater bursting along fault zones was further revealed. In the case study, different scenarios accounting for the different development phases of the fault weakness zone and groundwater bursting as well as different groundwater pressures were considered, and each of them was simulated using the 3D visual elastic–plastic numerical mechanics model FLAC^3D (Fast Lagrangian Analysis of Continua in 3 Dimensions). Through the case study, the applicability of rock mechanics modeling software for studying retarded groundwater bursting along a fault zone was examined. Several issues are discussed regarding the investigations of retarded groundwater bursting and a general structured approach to investigate and control this geological hazardous event is presented.     

城市土地需求分析

本文介绍城市土地需求预测的分析技术。城市土地需求预测的一般逻辑是：（1）首先根据城市功能预测住宅．工业和商业．公共空间所需的城市建筑空间需求；（2）根据市场或城市规划以及城市发展政策，确定城市发展强度；（3）最后确定土地发展参数。本文介绍的比例参数和线性外推方法简单、成本低、对数据要求不高，因此对中小城市或地区，特别是发展稳定的地区，是一个相对比较可行并且可靠的方法。     

Application of cross validation techniques for modelling construction costs during the very early design stage

Building client/owners need estimates of likely construction costs for budgeting purposes early in the procurement process when little detailed design information is available beyond the type, size and location of the facility. One of the more sophisticated techniques available for this purpose is the storey enclosure method, developed by James in 1954. This uses the basic physical measurements of the building envelope, together with an arbitrary set of multipliers, or weights, to forecast tender/bid prices. Although seldom used in practice, James succeeded in showing his method to be capable of significantly outperforming alternative approaches.

The research reported in this paper aimed firstly to reassess James’ claims with new data and secondly to advance his method by using regression techniques to obtain the weights involved. Based on data from 138 completed Hong Kong projects for four types of building, two types of regression models were developed. This involved the use of sophisticated features such as leave-one-out cross validation to simulate the way in which forecasts are produced in practice and a dual stepwise selection strategy that enhances the chance of identifying the best model. An algorithm was also designed to select the appropriate parametric and non-parametric tests for objective and rigorous model evaluation against alternatives.

The results indicate that, contrary to James’ claim, both his original method and the two regression-based alternatives are not significantly better or worse than other models. Surprisingly, the widely used floor area model was found to under-perform in terms of consistency for offices and private housing. For private housing in particular, it was felt that the storey enclosure method was likely to offer good prospects of improvement on those methods currently in use in practice.     

A new methodology for urban wind resource assessment

In the latest years the wind energy sector experienced an exponential growth all over the world. What started as a deployment of onshore projects, soon moved to offshore and, more recently to the urban environment within the context of smart cities and renewable micro-generation. However, urban wind projects using micro turbines do not have enough profit margins to enable the setup of comprehensive and expensive measurement campaigns, a standard procedure for the deployment of large wind parks. To respond to the wind assessment needs of the future smart cities a new and simple methodology for urban wind resource assessment was developed. This methodology is based on the construction of a surface involving a built area in order to estimate the wind potential by treating it as very complex orography. This is a straightforward methodology that allows estimating the sustainable urban wind potential, being suitable to map the urban wind resource in large areas. The methodology was applied to a case study and the results enabled the wind potential assessment of a large urban area being consistent with experimental data obtained in the case study area, with maximum deviations of the order of 10% (mean wind speed) and 20% (power density).     

陕西省烟草蚜传病毒的发生发展规律及预测预报模型的初步研究

陕西烟田蚜传病毒病的侵染源主要为油菜田,蚜传病毒病的发生程度与第一次迁入烟田的蚜虫量显著相关,以后烟田发病缓慢上升或维持一定水平,第二次迁入烟田蚜虫量与病毒病发生程度相关不大。预测模拟结果表明,第一次蚜虫迁飞高峰时百株烟草上有翅蚜量(X₁₂)、2~4月份降水量(X₄)、4月份平均气温(X₁₃)与烟草蚜传病毒病的病株率极显著相关,模拟所得方程为;Y=1.5973-0.0014X₄+0.6084X₁₂-0.1079X₁₃,回归检测结果历史符合率为98.5%以上。      

塔西南坳陷区域地震地层学研究

在对地震反射资料进行品质分析的基础上,采用地震反射特征的“多参数法”划分地震相,用优势地震相来代表各层序的地震相类型,然后将单井相剖面和地面地质相剖面转换成时间域剖面,对地震剖面上的地震相进行标定,建立地震相与沉积相的关系,进而将地震相转换为沉积相。依据层速度、剩余层速度和钻井资料,建立岩性与速度量板,进行区域岩性预测。