Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks

Abbot, J.; Marohasy, Jennifer

doi:10.1007/978-3-319-50127-7_6

Cited by 3 publications

(7 citation statements)

References 51 publications

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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%

“…We have also published a series of articles describing the application of ANNs to monthly rainfall forecasting in other parts of Australia including northern and central Queensland [11][12][13][15][16][17] the Murray Darling Basin [14] and Western Australia [19]. The forecasts for these regions also use a combination of large-scale climate indices and the local variables temperature and rainfall with lead times between 1 and 18 months.…”

Section: Other Ann Studiesmentioning

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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Section: Data Method and A First Studymentioning

confidence: 99%

Section: Progressing To Automation and Single-month Optimizationmentioning

confidence: 99%

Section: Other Ann Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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

Abbot¹,

Marohasy²

2018

Engineering and Mathematical Topics in Rainfall

Self Cite

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“…When time scales on the order of months or longer are involved, datasets are typically much smaller than those involving shorter time scales. A broad range of ML methods are applied, from simple methods like multilinear regression (MLR) up to advanced neural networks models [13,[16][17][18]20,21,24,25,46,47,49,[59][60][61][62]. Because of the small data sets used, researchers often perform feature selection/reduction to avoid overfitting.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

“…However, we found that many papers dealing with monthly prediction of climate parameters did not transform the input data to remove seasonality. Some papers accommodate seasonality by including data from month n − 12 to predict parameters at month n [13,17,19,20,24,25,27,28,44,49,51,58,60,62,67]. Month n's time stamp (defined as n mod 12) was used as a feature in [19,49], but is not common in the literature.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures

Abbot¹

2021

EARTH

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The validity and interpretation of differing representations of proxy temperature profiles from the past 2,000 years for the northern hemisphere remains controversial. One perspective of temperatures over the past 1,000 years embodies a major oscillation with a peak corresponding with the Medieval Warm Period (MWP), a trough representing the Little Ice Age (LIA) and subsequent increasing temperatures to the present. An alternate temperature perspective, known as the "hockey stick" exhibits a slow long-term cooling trend downward from about 1000 AD to about 1900 AD, followed by relatively rapid warming in the 20th century and is a prominent feature in describing the apparent climate crisis. The present study, using spectral analysis, shows that both types of profile have a dominant millennial oscillation and a set of lower power centennial and decadal oscillations. The key difference in determination of development of the proxy temperature profile into either a hockey stick or MWP_LIA cycle is the phase alignments of centennial and decadal oscillations with respect to the millennial oscillation. In both cases, the resultant sine waves from spectral analysis up to 1880 AD can be used to train a an artificial neural network using oscillatory data corresponding to the pre-industrial era, then forecasting temperatures into the 20 th century, enabling an estimation of natural and anthropogenic contributions to recent warming. The limitations of highly complex general circulation models that do not to adequately incorporate oscillatory patterns in temperatures may be a compelling reason to promote more extensive use of forecasting with established machine learning techniques such as ANNs.

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Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks

Cited by 3 publications

References 51 publications

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

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

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Using Oscillatory Processes in Northern Hemisphere Proxy Temperature Records to Forecast Industrial-era Temperatures

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