Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Abbot, J.; Marohasy, Jennifer

doi:10.2495/sdp-v12-n7-1117-1131

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…e artificial neuron gets the output of all neurons connected to it, and the signal to be generated is amplified connection strength [7]. e weighted total is compared to the net value of the neuron, and the fake neuron is triggered if it is bigger than the threshold [8,9].…”

Section: Overview Of Artificial Nnsmentioning

confidence: 99%

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Kong¹

2022

Security and Communication Networks

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From the environmental and security status quo faced by big data, although big data is unstructured or more difficult to filter and analyze, it does not mean that big data is necessarily more secure. In order to be invincible in the fierce market competition, an enterprise must conduct an in-depth understanding and investigation of the rapid changes in the market and economic development. The current era is already the Internet era. The arrival of the era of network communications has greatly facilitated people’s lives. However, how to seize the opportunities in the era of network communication and make economic predictions based on actual situations are particularly important for enterprises. Therefore, we are required to make reasonable economic predictions of network communication services in order to seize opportunities and meet challenges. By analyzing the development and application of artificial neural network (NN), this article briefly introduces its development and principles. Based on the reality of a network communication industrial enterprise, it uses data modeling and comparative analysis to establish a logistic regression model, a decision tree model, and a BP NN. The model compares the customer types and data predictions of the enterprise’s data traffic under the three models. From the results of the model analysis, it can be seen that in the analysis of the three models, the ROC curve analysis, the BP NN predicts that the cumulative hit rate in the ROC curve is wider. More users who can handle traffic services will be covered. In the cumulative customer lift analysis, under the first set of data (that is, when the depth is 20), the cumulative depths of the decision tree model, logic analysis model, and BP neural system prediction model are 2.5, 3.4, and 3.6, respectively. The BP neural system prediction model has the highest value of cumulative depth. In the cumulative capture response percentage analysis, the cumulative capture response percentage from low to low is the decision tree model and logic analysis. From BP NN prediction model, we can draw the conclusions. The network system can play a good role in forecasting and help enterprise managers make economic decisions.

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Section: Overview Of Artificial Nnsmentioning

confidence: 99%

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Kong¹

2022

Security and Communication Networks

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“…This chapter is a review of various studies undertaken since 2012 focused on this general area, with specific information on data and methodology in the published technical papers that are referenced [11][12][13][14][15][16][17][18][19][20][21]. However, in this first section, the method used in an early study [17] is provided in more detail, by way of background into how an ANN can be practically deployed to generate a rainfall forecast.…”

Section: Data Method and A First Studymentioning

confidence: 99%

“…In contrast, with the Neurosolutions Infinity software that we used since 2015 [19][20][21] the selection of network architecture and configuration was automated. This offered a great advantage in terms of arriving at an optimum forecast model for each data set of interest without prohibitive time outlay.…”

Section: Progressing To Automation and Single-month Optimizationmentioning

confidence: 99%

“…The isohyet map was constructed using Teraplot software -and the forecast are 12 months in advance using single-month optimization [21]. In Figures 4 and 5, it is evident that rainfall varies with topography, and was highest at the places of some altitude in both the relatively dry December of 2005, and also the flood month of December 2010.…”

Section: Regional Rainfall Forecastsmentioning

confidence: 99%

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Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

Abbot¹,

Marohasy²

2018

Engineering and Mathematical Topics in Rainfall

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The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in southeastern Queensland, Australia, for lead-times of 3-12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia.

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“…One of the best hydro-climatology data prediction methods is an artificial neural network (ANN) since it can make prediction with the input of compound data [4]. Rainfall predictions using artificial neural networks have been widely studied and reported [5][6][7][8][9][10]. Perceptron, Multilayer Perceptron (MLP), and Backpropagation are three types of ANN that are widely known.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2022

MMEP

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Artificial Neural Networks are widely used in prediction activities and classification processes. However, the implementation on average only uses a network architecture with one hidden layer, while the development of architectures with two or three hidden layers has not been done much. This article discusses the process of developing ANN Backpropagation using a Matlab-based graphical user interface with three hidden layers combined with non-linear activation functions (logsig, tansig, tanh) and training functions (trainrp and trainlm) based on learning rate and momentum. Architecture was created to study climate change in the area around Lombok International Airport station by training on hydrological data (rainfall) from January 2012 to December 2021 with a data type of 10-day interval (36 data every year). The number of neurons in the first hidden layer was determined using the Hecht-Nielsen model, while the second and third hidden layers used the Lawrence-Fredrickson model. Simulation results with architecture 36-73-37-19-1, a learning rate of 0.1, and momentum of 0.9 showed that variations in the activation function logsig-logsig-logsig-purelin and trainlm function demonstrated the best result with epoch of 7, MSE of 0.00090, and RMSE of 0.03011 in the training process and epoch of 5, MSE of 0.003758, and RMSE of 0.0613 in the data testing process. Furthermore, the prediction results demonstrated that a Q-value of 0.222 based on the Schmidt-Ferguson criteria obtained higher rainfall intensity information than previous years with climate category B (wet). Therefore, the government must be careful in determining policies related to field activities especially in agriculture because of climatic conditions with high rainfall.

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Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Cited by 8 publications

References 17 publications

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

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