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Application of artificial neural networks to Rainfall Forecasting in Queensland,Australia

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Advances in Atmospheric Sciences 2012年第4期29卷 717-730页

作者： John ABBOT Jennifer MAROHASY Centre for Plant and Water Science Central Queensland UniversityBruce Highway Rockhampton Q.4702Australia

In this study, the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland, Australia, was assessed by inputting recognized climate indices, monthly historical rainfall data, and atmospheric temperatures into a prototype stand-alone, dynamic, recurrent, time-delay, artificial neural network. Outputs, as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009, were compared with observed rainfall data using time-series plots, root mean squared error （RMSE）, and Pearson correlation coefficients. A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology＇s Predictive Ocean Atmosphere Model for Australia （POAMA）-I.5 general circulation model （GCM） indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared. The application of artificial neural networks to rainfall forecasting was reviewed. The prototype design is considered preliminary, with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

关键词： general circulation models artificial neural networks rainfall forecast

Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis

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World Journal of Gastroenterology 2005年第37期11卷 5867-5873页

作者： Edith Lahner Enzo Grossi Marco Intraligi Massimo Buscema Vito D Corleto Gianfranco Delle Fave Bruno Annibale Digestive and Liver Disease Unit Ⅱ Medical School Sant' Andrea Hospital University"La Sapienza" Rome Italy Enzo Grossi Bracco Imaging Spa Milan Italy Semeion Research Center Rome Italy

AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the ***: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with *** 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3:input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5:use of only serological ***: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%,respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were,respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA,may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.

关键词： Atrophic body gastritis Computer-based decision support Gastroscopy artificial neural networks

Vulnerability Assessment Method for Immovable Cultural Relics Based on artificial neural networks—An Example of a Heavy Rainfall Event in Henan Province

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International Journal of Disaster Risk Science 2023年第1期14卷 41-51页

作者： Can Xu Adu Gong Long Liang Xiaoke Song Yi Wang State Key Laboratory of Remote Sensing Science Beijing Normal UniversityBeijing100875China Beijing Key Laboratory of Environmental Remote Sensing and Digital City Beijing Normal UniversityBeijing100875China Faculty of Geographical Science Beijing Normal UniversityBeijing100875China

Cultural relic conservation capability is an important issue in cultural relic conservation research,and it is critical to decrease the vulnerability of immovable cultural relics to rainfall *** used vulnerability assessment methods are subjective,are mostly applied to regional conditions,and cannot accurately assess the vulnerability of cultural *** addition,it is impossible to predict the future vulnerability of cultural ***,this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard *** extreme rainfall event in Henan Province in 2021 was selected as an example,with a survey report on the damage to cultural relics as a *** results imply that the back propagation(BP)neural network-based method of assessing the vulnerability of immovable cultural relics is reliable,with an accuracy rate higher than 92%.Based on this model to predict the vulnerability of Zhengzhou City’s cultural relics,the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were *** them,the vulnerability of ancient sites is higher than those of other cultural relic *** assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.

关键词： artificial neural networks Henan Province Immovable cultural relics Rainfall hazards Vulnerability assessment

Forecasting Monsoon Precipitation Using artificial neural networks

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Advances in Atmospheric Sciences 2001年第5期18卷 950-958页

作者：曹鸿兴魏凤英 School of Business Systems Faculty of Information Technology Monash University Australia Chinese Academy of Meteorological Sciences Beijing China

This paper explores the application of artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of artificial neural networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

关键词： forecasting monsoon precipitation artificial intelligent technique artificial neural networks

Comparison of artificial neural networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

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Chinese Geographical Science 2017年第5期27卷 747-759页

作者： Samad EMAMGHOLIZADEH Shahin SHAHSAVANI Mohamad Amin ESLAMI 1.Department of Water and Soil Engineering Shahrood University of TechnologyShahrood361999-5161Iran

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

关键词： precision agriculture soil characteristics interpolation artificial neural networks geographically weighted regression Cokriging

Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil

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Petroleum Science 2010年第1期7卷 118-122页

作者： Dong Xiucheng Wang Shouchun Sun Renjin Zhao Suoqi School of Business Administration China University of Petroleum Beijing 102249 China School of Chemical Science and Engineering China University of Petroleum Beijing 102249 China

Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.

关键词： Saturates vacuum gas oil prediction artificial neural networks genetic algorithm

Long-Term Electricity Demand Forecasting for Malaysia Using artificial neural networks in the Presence of Input and Model Uncertainties

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Energy Engineering 2021年第3期118卷 715-725页

作者： Vin Cent Tai Yong Chai Tan Nor Faiza Abd Rahman Hui Xin Che Chee Ming Chia Lip Huat Saw Mohd Fozi Ali Centre for Modelling and Simulation Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling JayaMalaysia Mechanical Engineering Department Faculty of EngineeringBuilt Environment and Information TechnologySEGi UniversityPetaling JayaMalaysia Lee Kong Chian Faculty of Engineering and Science UTARKajangMalaysia Faculty of Civil Engineering Universiti Teknologi MARAShah AlamMalaysia

Electricity demand is also known as load in electric power *** article presents a Long-Term Load Forecasting(LTLF)approach for *** artificial neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this *** of input variables and ANN model were introduced to obtain the prediction for years 2022 to *** correlation was used to examine the input variables for model *** analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly *** forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)*** R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output *** proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for *** the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

关键词： Long-term load forecasting SARIMA artificial neural networks uncertainty analysis Malaysia

Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation

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International Journal of Modeling, Simulation, and Scientific Computing 2016年第2期7卷 88-108页

作者： David Ifeoluwa Adelani Mamadou Kaba Traore Department of Computer Science African University of Science and Technology AbujaNigeria LIMOS CNRS UMR 6158 Universit´e Blaise PascalClermont-Ferrand 2 Campus des Cezeaux63173 Aubiere

artificial neural networks(ANNs),a branch of artificial intelligence,has become a very interesting domain since the eighties when back-propagation(BP)learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear *** is used extensively to solve complex nonalgorithmic problems such as prediction,pattern recognition and ***,in the context of a holistic study,there may be a need to integrate ANN with other models developed in various paradigms to solve a *** this paper,we suggest discrete event system specification(DEVS)be used as a model of computation(MoC)to make ANN models interoperable with other models(since all discrete event models can be expressed in DEVS,and continuous models can be approximated by DEVS).By combining ANN and DEVS,we can model the complex configuration of ANNs and express its internal ***,we are extending the DEVS-based ANN proposed by Toma et al.[A new DEVS-based generic art-ficial neural network modeling approach,The 23rd European Modeling and Simulation Symp.(Simulation in Industry),Rome,Italy,2011]for comparing multiple configuration parameters and learning algorithms and also to do *** DEVS models are described using the high level language for system specification(HiLLS),[Ma¨ıga et al.,A new approach to modeling dynamic structure systems,The 29th European Modeling and Simulation Symp.(Simulation in Industry),Leicester,United Kingdom,2015]a graphical modeling language for *** developed platform is a tool to transform ANN models into DEVS computational models,making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation(MAS).

关键词： artificial neural networks DEVS Z-schema reusability interoperability HiLLS learning algorithm modeling and simulation

Comparative Study of Reverse Algorithms via artificial neural networks Based on Simulated Indentation Tests

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Tsinghua Science and Technology 2008年第S1期13卷 393-399页

作者： Somsak Swaddiwudhipong Edy Harsono Department of Civil Engineering National University of Singapore

The advances in the instrumented indentation equipments and the need to assess the properties of materials of small volume such as those constitute the micro-electro-mechanical devices, micro-electronic packages, and thin films have propelled the interest in material characterization via indentation tests. The load-displacement curves and their characteristics, namely, the curvature of the loading path, C, and the ratio of the remaining and total work done, WR / WT, can be conveniently obtained from finite element simulations for various elasto-plastic material properties. The paper reports the comparative study on two reverse neural networks algorithms involving several combinations of databases established from the results obtained from simulated indentation tests. The performance of each set of results is analyzed and the most appropriate algorithm identified and reported. The approach with the selected neural networks model has great potential in practical applications on the characterization of a small volume of materials.

关键词： artificial neural networks finite element simulation friction least square support vector machines material characterization indentation tests