Estimating rainfall using machine learning strategies based on weather radar data

Srinivas, T.; Ramasubbareddy, Somula; Govinda, K.; Saxena, A.; Reddy, A. Pramod

doi:10.1002/dac.3999

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…This means that RMSE is most useful when big mistakes are particularly unnecessary. 24,25 The MAE and RMSE can be used together in a series of forecasts to identify the variability in the errors as shown in Table 2. The RMSE will often be superior or equal to the MAE; the greater their variance, the greater the sample variance in the individual errors.…”

Section: Performance Metricsmentioning

confidence: 99%

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

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In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

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Section: Performance Metricsmentioning

confidence: 99%

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Bhagavathi

Thavasimuthu

Murugesan

et al. 2021

Int J Communication

138

View full text Add to dashboard Cite

show abstract

“…Both the Z and Z DR fields need to be corrected from attenuation before applied in the precipitation classification and QPE. Different attenuation correction methods were proposed using the differential phase (φ DP ) measurement such as the linear φ DP approach, the standard ZPHI method, and the iterative ZPHI method (e.g., Jameson, 1992;Carey et al, 2000;Testud et al, 2000;Park et al, 2005). Because of its simplicity and easy implementation in a real-time system, the linear φ DP method was applied in the current work.…”

Section: Input Polarimetric Radar Variables and Preprocessesmentioning

confidence: 99%

“…Artificial intelligence (AI) algorithms using meteorological radar data have been developed well during the past 2 decades. With the assistance of AI, weather radar's capabilities in severe weather prediction, rainfall rate estimation, and lightning detection have apparently been improved (e.g., Capozzi et al, 2018;T. et al, 2019;Yen et al, 2019).…”

Section: Introduction Of Svmmentioning

confidence: 99%

Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method

Wang

Lin

Chang³

et al. 2021

Atmos. Meas. Tech.

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Abstract. A precipitation separation approach using a support vector machine method was developed and tested on a C-band polarimetric weather radar located in Taiwan (RCMK). Different from those methods requiring whole-volume scan data, the proposed approach utilizes polarimetric radar data from the lowest unblocked tilt to classify precipitation echoes into either stratiform or convective types. In this algorithm, inputs of radar reflectivity, differential reflectivity, and the separation index are integrated through a support vector machine. The weight vector and bias in the support vector machine were optimized using well-classified data from two precipitation events. The proposed approach was tested with three precipitation events, including a widespread mixed stratiform and convective event, a tropical typhoon precipitation event, and a stratiform-precipitation event. Results from the multi-radar–multi-sensor (MRMS) precipitation classification algorithm were used as the ground truth in the performance evaluation. The performance of the proposed approach was also compared with the approach using the separation index only. It was found that the proposed method can accurately classify the convective and stratiform precipitation and produce better results than the approach using the separation index only.

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“…This model can also handle regression and classification problems, and it has the ability to handle missing data [5]. In a study by Srinivas, et al [6] on rainfall prediction, they compared XGBoost with other machine learning methods, including LSTM and RF, and discovered that XGBoost performed better than them in terms of accuracy and efficiency. The performance of XGBoost is 99% accuracy, while RF gives 92% accuracy, and LSTM gives an accuracy of 42% [6].…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Machine learning classification of rainfall forecasts using Austin weather data

Tin,

Sheng,

Xian

et al. 2024

ijirss

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The paper examines the machine learning classification of rainfall forecasts using Austin weather data. Rain is a natural phenomenon that is essential for the Earth's water cycle. Rain brings benefits to daily lives and also causes disasters, such as floods, which will endanger lives in addition to causing great losses. Due to this, many methods have been studied and experimented with to find a solution to predict rainfall and prevent tragedies from happening. In this research, the Austin weather dataset is applied to make predictions of rainfall through the implementation of machine learning models. The models used to predict rainfall based on the data set were Extreme Gradient Boosting, Support Vector Machine, Long Short-Term Memory, and Random Forest models. 21 variables with 1319 records were present in the dataset, but the variables used for the modelling were 18 variables from the original data, and 1 variable, “Precipitation Sum,” was converted to the variable “Precipitation Range,” which contained the classes “no rain,” “small rain,” “moderate rain,” and “heavy rain” based on specific value ranges. After training and predicting the data on the models, it was shown that Extreme Gradient Boosting gave the best results of 85.17% accuracy, 83.19% F1 score, 85.17% recall score, and 82.14% precision score, and was able to give predictions on all 4 classes of rainfall. This study and the way to implement machine learning models for rainfall prediction have the potential to provide new insights and methodologies for future studies and pave the way for finding a high-accuracy rainfall prediction method to avoid disaster.

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Estimating rainfall using machine learning strategies based on weather radar data

Cited by 10 publications

References 28 publications

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method

Machine learning classification of rainfall forecasts using Austin weather data

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