ClimateLearn: A machine-learning approach for climate prediction using network measures

Feng, Qu; Vasile, Ruggero; Segond, Marc; Gozolchiani, Avi; Wang, Yang; Abel, Markus; Havlin, Shilomo; Bunde, Armin; Dijkstra, Henk A.

doi:10.5194/gmd-2015-273

Cited by 43 publications

(21 citation statements)

References 30 publications

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“…Most of the ANN studies to predict El Niño used simple architectures with a single hidden layer. Recently deeper architectures have been successfully tested [72,73]. Nevertheless, a very complex ANN architecture will face the problem of overfitting, since the available time series are not very long and the number of parameters to optimize grows rapidly with ANN complexity.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…A first effort to combine complex network metrics with ANN's for the prediction of the NINO3.4 index was made in Feng et al [72]. They considered the classification problem (determining if El Niño will occur) with an ANN (two hidden layers with three neurons each) in which attributes were only the climate-networkbased quantities from Gozolchiani et al [64].…”

Section: Recent Ml-based Predictionsmentioning

confidence: 99%

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The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

et al. 2019

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We review prediction efforts of El Niño events in the tropical Pacific with particular focus on using modern machine learning (ML) methods based on artificial neural networks. With current classical prediction methods using both statistical and dynamical models, the skill decreases substantially for lead times larger than about 6 months. Initial ML results have shown enhanced skill for lead times larger than 12 months. The search for optimal attributes in these methods is described, in particular those derived from complex network approaches, and a critical outlook on further developments is given.

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Section: Discussion and Outlookmentioning

confidence: 99%

Section: Recent Ml-based Predictionsmentioning

confidence: 99%

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

et al. 2019

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“…They concluded as the proposed system enhances the performance in terms of precision and efficiency. Q. Feng, X. Wen, and J. Li [15] implemented wavelet analysis-support vector machine coupled model (WA-SVM) for predicting rainfall for next 1, 3 and 6 months. This model was obtained by a hybrid of discrete wavelet transform (DWT) and support vector machine (SVM) methods.…”

Section: Related Workmentioning

confidence: 99%

“…4, 5,6 are describes the residual plot, trends of actual & predicted values and residual respectively of the proposed model. [15] model is a combined of wavelet analysis and support vector machine used to predict short term rainfall of next 1, 3, 6 months based on existing weather datasets. WA-SVM model is evaluated and it gives results as RMSE (12.689) and MAE (7.828).…”

Section: Figure 4 a Residual Plot Of The Emlr Modelmentioning

confidence: 99%

An Adaptive Model for Forecasting Seasonal Rainfall Using Predictive Analytics

Reddy¹,

Sureshbabu²

2019

IJIES

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India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into orientation in the farming sector to decide the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and the Hadoop file system. In the proposed model (EMLRM) first, we stored the unstructured weather data in hadoop distributed file system (HDFS), process that stored data by using MapReduce Algorithm and build the rainfall prediction model by utilizing Multiple Linear Regression.We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. The experimental outcomes show that the EMLRM provided the lowest value of Root Mean Square Error (RMSE= 0.274) and Mean Absolute Error (MAE= 0.0745) compared with existing methods. The results of the analysis will help the farmers to adopt effective modeling approach for predicting long-term seasonal rainfall.

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“…Complex networks turn out to be an efficient way to represent spatiotemporal information in climate systems (Tsonis et al, 2006;Steinhaeuser et al, 2012;Fountalis et al, 2015) and can be used as an attribute reduction technique. These climate networks are in general constructed by linking spatiotemporal locations that are significantly correlated with each other according to some measure.…”

Section: Introductionmentioning

confidence: 99%

Using network theory and machine learning to predict El Niño

et al. 2018

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Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag time of 6 months. In this paper, we aim to increase this prediction skill at lag times of up to 1 year. The new method combines a classical autoregressive integrated moving average technique with a modern machine learning approach (through an artificial neural network). The attributes in such a neural network are derived from knowledge of physical processes and topological properties of climate networks, and they are tested using a Zebiak-Cane-type model and observations. For predictions up to 6 months ahead, the results of the hybrid model give a slightly better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Interestingly, results for a 12-month lead time prediction have a similar skill as the shorter lead time predictions.

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ClimateLearn: A machine-learning approach for climate prediction using network measures

Cited by 43 publications

References 30 publications

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

An Adaptive Model for Forecasting Seasonal Rainfall Using Predictive Analytics

Using network theory and machine learning to predict El Niño

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