Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

Analysis of radar images for rainfall forecasting using neural networks

This paper describes a new approach to the analysis of weather radar data for short-range rainfall forecasting based on a neural network model. This approach consists in extracting synthetic information from radar images using the approximation capabilities of multilayer neural networks. Each image in a sequence is approximated using a modified radial basis function network trained by a competitive mechanism. Prediction of the rain field evolution is performed by analysing and extrapolating the time series of weight values. This method has been compared to the conventional cross-correlation technique and the persistence method for three different rainfall events, showing significant improvement in 30 and 60 min ahead forecast accuracy.     

基于递归神经网络的风暴潮增水预测

风暴潮增水的准确预测能极大地减少人员伤害和经济损失,具有重要的实用价值。传统的风暴潮预报方法主要包括经验和数值预报,很难建立起相对准确的模型。现有的基于机器学习风暴潮预报方法大都只提取出静态数据间的关系,并没有充分挖掘出风暴潮数据背后的时序关联特性。文中提出了一种基于递归神经网络的风暴潮增水预测方法。本文对风暴潮时序数据进行特定的处理,并设计合适结构的递归神经网络,从而完成时序数据的预测。相较于传统的BP神经网络,递归神经网络能更好地应对时序数据的预测问题。将该方法用于潍坊水站的增水预测中,结果表明,相对于BP神经网络,递归神经网络能得到更好的预测结果,误差更小。     

Performance comparison of artificial neural network models for daily rainfall prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.     

基于小波网络的动态系统辨识方法及应用*

本文介绍了一种多输入非线性动态系统辨识算法，基于该算法的非线性辨识系统成功用于局部地区短时暴雨的预报。在这个系统中我们采用一种小波网络来追踪非线性系统的动态性，用一种基于小波逼近的非参数估计方法用于系统的状态空间模型的辨识中。从实验结果可看出，与传统的神经网络方法相比，该系统在速度、可靠性以及精确度上都有了很大的提高。     

基于遗传神经网的沙尘暴预测模型的研究

神经网络结构和学习算法在很大程度影响模型建立的时间和预测结果的准确性,论文针对现有BP神经网络的缺陷,提出了基于遗传算法的神经网络预测模型,并利用该模型研究了沙尘暴的预测问题。仿真预测研究表明,提出的方法无论是收敛速度方面还是预报准确率方面,都取得了比较满意的结果。     

Knowledge acquisition using a neural network for a weather forecasting knowledge-based system

Neural network technology is experiencing rapid growth and is receiving considerable attention from almost every field of science and engineering. The attraction is due to the successful application of neural network techniques to several real world problems. Neural networks have not yet found widespread application in weather forecasting. The reason for this has been the difficulty in obtaining suitable weather forecasting data sets. In this paper we describe our experience in applying neural network techniques for acquiring the necessary knowledge to predict the weather conditions of Melbourne City and its suburbs in Australia during a 24 hour period beginning at 9 am local time. The accuracy of forecasts produced by a given forecasting procedure typically varies with factors such as geographical location, season, categories of weather, quality of input data, lead time and validity time. Two types of weather data sets assembled from the archives of the Australian Commonwealth Bureau of Meteorology are used for training the neural network. The results of the experiments are competitive and are discussed.     

Multi-technique observations on precipitation and other related phenomena during cyclone Aila at a tropical location

A severe cyclone, named Aila, passed over Kolkata on 25 May 2009. The strong convective activities manifested through rainfall during the cyclone were studied with multi-technique observations involving micro rain radar, disdrometer, rain gauges, and a Ku-band satellite signal receiving system. A number of features of precipitation, namely the presence of large rain drops, the large vertical extent of the precipitating layer, an unusual enhancement of cross-polar component, and strong scintillations of the co-polar component of the satellite signal due to strong turbulences associated with the cyclone, were observed. This study leads to a more comprehensive understanding of the precipitation associated with a cyclonic storm.     

A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

The internal structure of a complex system can manifest itself with correlations among its components. In global business, the interactions between different markets cause collective lead–lag behavior having special statistical properties which reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic algorithm (GA) with neural network (NN) is proposed to take advantage of the lead–lag dynamics, to make the NN forecasting process more transparent and to improve the NN’s prediction capability. Two business case studies are carried out to demonstrate the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting. Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.     

公共卫生事件监测与预警系统

在现有公共卫生体系基础上,提出了建立公共卫生事件监测与预警系统的框架模型,并首次利用神经网络的基本原理,将改进的Back-Propagation算法应用于系统的核心预测模型。该系统可以通过监测医疗数据的变化情况来迅速预测出疾病的发生和未来的发展趋势,经初步模拟研究,预测精度可达93%,为公共卫生事件的长期可预测性提供了一种新的途径。     

Design and implementation of NN5 for Hong Kong stock price forecasting

A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN). In this paper, the theoretical background of NNs and the backpropagation algorithm is reviewed. Subsequently, an attempt to build a stock buying/selling alert system using a backpropagation NN, NN5, is presented. The system is tested with data from one Hong Kong stock, The Hong Kong and Shanghai Banking Corporation (HSBC) Holdings. The system is shown to achieve an overall hit rate of over 70%. A number of trading strategies are discussed. A best strategy for trading non-volatile stock like HSBC is recommended.     

混合模型神经网络在短期负荷预测中的应用

提出了可应用于电力系统负荷预测的混合模型神经网络方法,该方法同时具有电力系统负荷预测的传统方法的优点及人工视网络方法的优点,该方法中,不同的负荷分量采用不同类型的预测方法,并采用基本绵谐振分量作神经网络的输入,神经网络的训练采用快速的学习算法进行,该方法具有很强的实时性和适应性,适用于没有气象资料的应用场合,仿真计算的结果表明,预测精度较传统来得高。     

一种基于压力敏感元件的降雨传感器

针对已有降水传感器对强降水测量误差大、测量范围小等不足,研制了一种基于压力敏感元件的雨量智能传感器。设计的传感器硬件由压力敏感元件核心的降雨物理量转换单元、以差分放大和线性处理模拟电路为主的信号处理单元、以高速低功耗嵌入式处理器ARM9为核心的数据计算单元等部分组成;传感器软件上采用广义回归神经网络的函数逼近方法,通过小样本训练建立精确测量模型。测量降雨作用在压力传感器上的压力大小及其对时间的变化,利用广义回归神经网络模型可计算得到精确雨量。试验表明,该传感器不仅解决了强降水等原因引起的雨量测量不准的问题,具有测量量程宽、精度高等优点。     

Analysis of early warning of corporate financial risk via deep learning artificial neural network

To forecast the financial crisis of manufacturing corporations more accurately, a risk warning model of corporate finance is constructed based on back propagation (BP) neural network to forecast the financial crisis. Firstly, based on the principle of index selection, the forecast indexes are selected and the index system of financial risk early warning is constructed. Then the index system is optimized by factor analysis. Finally, the BP neural network algorithm model is adopted to forecast the financial crisis of 200 manufacturing corporations in 2018 and 2019, and the forecasting results are compared with the traditional method. The results show that the prediction accuracy of the enterprise financial risk early warning model based on the BP neural network for 2018 is above 85%, and the prediction accuracy for 2019 is above 95%, or even 100%. Through comparison with other traditional methods, the prediction accuracy of the BP neural network in 2018 (above 88%) is higher than that of other algorithms (below 87%). In 2019, the prediction accuracy of BP neural network (above 90%) is higher than other algorithms (less than 88%). The accuracy of the proposed financial risk warning model is 95%, and the accuracy is at least 2% higher than traditional method, which prove that the risk early warning model constructed in this study can accurately forecast the financial crisis of the corporation. This study is of important reference value for the establishment of efficient financial crisis forecasting model under deep learning.     

Tourism demand forecasting using novel hybrid system

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.     

湖库水质监测与水华预警信息系统

针对当前湖库水质监测及水华预测预警信息化发展相对落后的现状,开发一套集水质监测、水华预测预警功能于一体的智能化信息系统。采用Visual Studio 2010中的C++语言进行系统平台搭建,将网络通信、地理信息系统、SQL2005数据库等技术相结合,对湖库水质信息进行实时监测,并通过灰色-BP神经网络模型实现对湖库藻类水华较高精度的中长期预测预警的功能,为环保部门进行湖库水华防治提供有效的信息化决策平台。     

Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong

The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem. Ensemble algorithms are proposed to improve the classifier's accuracy. Moreover, a distance-based regional data set was generated to capture ozone transportation characteristics. The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances. Furthermore, this study advises on the relative importance of the different variables for ozone pollution prediction and confirms that regional data facilitate better forecasting. The results of this research can be promoted by the Hong Kong authorities for improving the existing forecasting tools. Moreover, the results can facilitate researchers' selection of the appropriate techniques in their future research.     

Key issues in the management of information systems: A Hong Kong perspective

The results of a survey are presented on the perceived importance of 20 IS issues in Hong Kong in 1994 and in the next five years. The survey was based on studies carried out in the USA and Taiwan. The major issues which changed were a decline in the importance of recruiting and maintaining a skilled workforce (IS human resources), accompanied by an increase in the importance of the need for a good communications network (Telecommunications). Comparing the results with those of the USA and Taiwan revealed seven issues common to all the three studies. In particular, the ability to anticipate and adapt to the changing face of IS (IS strategic planning) was a top three issue for the USA, Taiwan and Hong Kong.     

Statistical fuzzy interval neural networks for currency exchange rate time series prediction

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.     

The Relationship Between Cyberbalkanization and Opinion Polarization: Time‐Series Analysis on Facebook Pages and Opinion Polls During the Hong Kong Occupy Movement and the Associated Debate on Political Reform

Online activity is often cyberbalkanized, but it remains unclear whether this phenomenon leads to polarization of public opinion or if the relationship works in the reverse direction. This study tested the temporal association between cyberbalkanization and opinion polarization during the debate on political reform in Hong Kong. Online communities were constructed by a post‐sharing network of 1,644 Facebook pages (101,410 shares); the differences between intra‐ and inter‐community shares were derived, and a cyberbalkanization index was computed. A time‐series analysis showed that the index temporally preceded the opinion polarization, i.e., most of the opinion poll's respondents gave extreme ratings to government leaders, but not vice versa. The index was particularly predictive of polarization among youth.