South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach

Monego, Vinicius Schmidt; Anochi, Juliana Aparecida; Velho, Haroldo Fraga de Campos

doi:10.3390/atmos13020243

Cited by 14 publications

(12 citation statements)

References 25 publications

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“…Weather forecasts are extremely important as many industries such as agriculture, shipping, engineering, construction, natural disasters, aviation, and defence rely heavily on weather dynamics for successful operations [1]. For example, while deciding whether to plant, weed, or spray plants or animals, a farmer has to know if it will rain or not.…”

Section: Introductionmentioning

confidence: 99%

“…This simplifies configuration for operational use. It is important to note that NWP models require large datasets [1], which in turn requires expensive physical hardware and significant computational power [5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the benefits of using machine learning for weather and climate forecasting is reduced computing power [1]. Several researchers have used meteorological data from many countries to perform experiments to improve daily, monthly, and yearly precipitation prediction.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Gahwera,

Eyobu,

Isaac

2024

IEEE Access

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As a result of climate change, the difficulty in the prediction of short-term rainfall amounts has become a necessary area of research. The existing numerical weather prediction models have limitations in precipitation forecasting especially due to high computation requirements and are prone to errors. Precipitation amount prediction is challenging as it requires knowledge on a variety of environmental phenomena, such as temperature, humidity, wind direction, and more over a long period of time. In this study, we first of all present our Lake Victoria Basin weather dataset and then use it to conduct a rigorous analysis of machine learning algorithms to do short term rainfall prediction. The rigorous analysis includes algorithm optimizations to improve prediction performance. In particular, we intend to validate our weather dataset using various machine learning regression models which include Random Forest regression, Support vector regression, Neural Network regression, Least Absolute Shrinkage and Selection Operator regression, Gradient boosting regression, and Extreme Gradient boosting regression. The performance of the models was assessed using Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) performance metrics. The findings demonstrate that, in comparison to other algorithms, Extreme Gradient Boost Regression had the lowest MAE values of 0.006, 0.018, 0.005 for Lake Victoria basin weather data in Uganda, Kenya, and Tanzania respectively.INDEX TERMS Precipitation Amount, Weather Prediction, Data-driven Approaches, Short-term Forecasting.WRF model results can be automated and checked with

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Gahwera,

Eyobu,

Isaac

2024

IEEE Access

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“…This sometimes even leads to a low accuracy and a failure. In order to overcome limitations of these data classification methods, the correlation between performances of classification methods and datasets of a specific problem is studied (Song et al, 2012;Ohbyung et al, 2013), and an ensemble learning scheme is usually adopted to solve an intricate or large-scale problem (Nanni et al, 2015;Dong et al, 2020), such as AdaBoost (Creamer et al, 2010;Asteris et al, 2022), Bagging (Breiman, 1996), Stacking (Ribeiro et al, 2020) and Gradient Boosting (Monego et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Rayleigh-quotient-based Data Classification Method for Machine Learning

Yuanxiu

2023

Preprint

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Inspired by the ability of human ears to distinguish between different kinds of music and the ability of an ancient instrument to play countless beautiful melodies, this paper proposes the Rayleigh-quotient-based data classification method, which implements data classification by selecting an appropriate eigenfunction coordinate system to represent different categories of data and making different categories of data show significant differences in the coordinate components, meanwhile, transforming the selection problem of an eigenfunction coordinate system into a design problem of physical parameters of music instruments, such as designing a non-uniform string or membrane with distributed supports. In theory, the method is based on the eigenvalue problem for Sturm-Liouville differential equation, the eigenvalue problem for functional minimization, and the generalized eigenvalue problem for matrices, which have a time-honored history and can provide strong theoretical foundation and support. In addition, each parameter in the method has a clear physical interpretation and can be adjusted under the guidance of a set of complete theory, and their influence on classification results is definite, avoiding the large time consumption due to randomly adjusting parameters. The data classification procedure is divided into 2 steps, including mass density function design and stiffness function design, which not only simplifies the process of parameter adjustment, but also enables the optimization algorithm to be implemented by the linear programming with a low computational complexity, theoretically guaranteeing a high computational efficiency. Considering the amount of data being classified is large, two kinds of algorithms are developed to implement data classification, where one is a statistical averaging algorithm and the other is an incremental iterative algorithm. Finally, by combining the image database MNIST, the Rayleigh-quotient-based data classification method is employed to build an image classifier, which is trained on the MNIST training set including 60,000 images, and is tested on the MNIST test set including 10,000 images. The classification results show that by selecting an appropriate eigenfunction coordinate system, the Rayleigh quotient distribution of each category of image can be controlled within a very narrow range, while different categories of images show significant difference in the Rayleigh quotient distribution; the classification accuracy on the MNIST training set and on the MNIST test set are stable at about 99.50%, and the maximum accuracy can reach to 100.00% on the MNIST training set and 99.76% on the MNIST test set; and the Rayleigh-quotient threshold can be selected within a very wide range, beneficial for the robustness and stability of data classification.

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“…For this goal, several machine learning algorithms have been investigated, each with its own set of strengths and applications. For example, artificial neural networks (ANN) [22,23], recurrent neural networks (RNN) [24,25], random forest (RF) [26], gaussian process regression (GPR) [27], gradient-boosting [28], extreme gradient boosting [29], and long-short term memory (LSTM) [30].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Kumar,

Kedam,

Sharma

et al. 2023

Sustainability

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Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

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South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach

Cited by 14 publications

References 25 publications

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Rayleigh-quotient-based Data Classification Method for Machine Learning

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

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