Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

Salcedo‐Sanz, Sancho; Deo, Ravinesh C.; Carro-Calvo, L.; Saavedra-Moreno, B.

doi:10.1007/s00704-015-1480-4

Cited by 89 publications

(42 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They found that RBT approach performed better in reducing biases when compared with the raw EM, the EM with simple additive bias correction and the single best model. ML has also been applied in climate projections to statistically downscale monthly temperature and rainfall with different input (predictive) variables (Salcedo‐Sanz et al ., ; Sarhadi et al ., ; Vu et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…These two methods work well with few parameters and are easy to implement. For instance, SVM has been successfully applied in some recent cases such as managing nonlinear meteorological events (Salcedo‐Sanz et al ., ; Sarhadi et al ., ). On the other hand, RF and SVM have been proven to perform better compared to other ML techniques in some agriculture‐related areas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia

Wang¹,

Zheng

Liu

et al. 2018

Intl Journal of Climatology

117

View full text Add to dashboard Cite

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi-model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia

Wang¹,

Zheng

Liu

et al. 2018

Intl Journal of Climatology

117

View full text Add to dashboard Cite

show abstract

“…Accurate forecasting of daily land surface temperature (LST) is highly important for various fields, including weather maintenance services, agriculture, eco-environment, and industry [ 1 ]. Daily LST forecasting is the main forecasting factor in the daily weather forecast system [ 2 ]. In agriculture, daily LST forecasting can be adopted for agriculture irrigation systems, pest management schemes and diseases warring systems to predict the crop growth conditions that are useful for scheduling proper actions for drought development, as well as trends in the spread of diseases and pests [ 1 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

View full text Add to dashboard Cite

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

show abstract

“…Salcedo-Sanz et al [36] propose a Machine Learning model for long-term air temperature prediction, a task that is useful for a plethora of domains, such as agriculture. They use the publicly available monthly mean temperature dataset provided by the Australian Bureau of Meteorology (BOM), from urban and regional areas in Australia.…”

Section: Ml-based Forecasting On Numerical Datamentioning

confidence: 99%

A Survey on Intelligent Agricultural Information Handling Methodologies

et al. 2019

View full text Add to dashboard Cite

The term intelligent agriculture, or smart farming, typically involves the incorporation of computer science and information technologies into the traditional notion of farming. The latter utilizes plain machinery and equipment used for many decades and the only significant improvement made over the years has been the introduction of automation in the process. Still, at the beginning of the new century, there are ways and room for further vast improvements. More specifically, the low cost of rather advanced sensors and small-scale devices, now even connected to the Internet of Things (IoT), allowed them to be introduced in the process and used within agricultural production systems. New and emerging technologies and methodologies, like the utilization of cheap network storage, are expected to advance this development. In this sense, the main goals of this paper may be summarized as follows: (a) To identify, group, and acknowledge the current state-of-the-art research knowledge about intelligent agriculture approaches, (b) to categorize them according to meaningful data sources categories, and (c) to describe current efficient data processing and utilization aspects from the perspective of the main trends in the field.

show abstract

Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

Cited by 89 publications

References 30 publications

Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia

Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

A Survey on Intelligent Agricultural Information Handling Methodologies

Contact Info

Product

Resources

About