Machine Learning based Rainfall Prediction

Grace, R. Kingsy; Suganya, B.

doi:10.1109/icaccs48705.2020.9074233

Cited by 30 publications

(10 citation statements)

References 8 publications

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“…Method and Linear regression Techniques One of the significant deterrents that climate determining faces is the inappropriate assumptions from the nature. Climate determining falls under prescient mining which points on the investigation of the information, assessing the data set, and predicts he highlights of mysterious information [16]- [18].…”

Section: A Weather Prediction Using Normal Equationmentioning

confidence: 99%

Tenki Weather Prediction Model

Manimegalai

Muthu

2023

Preprint

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The activities of most sectors rely upon the climate. The environment is changing at an exceptional rate these days, which makes the old forecast strategies less powerful. To foresee the climate, a model is proposed, which combines data preparing, sensors, Google Dataset and machine learning to provide real-time weather monitoring and accomplish climate figures through prescient models valuable for individuals to know the climate conditions constantly.

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Section: A Weather Prediction Using Normal Equationmentioning

confidence: 99%

Tenki Weather Prediction Model

Manimegalai

Muthu

2023

Preprint

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“…Rainfall prediction is quite challenging due to dynamic changes in atmospheric processes. Researchers from several fields struggle to create prediction models for rainfall that is accurate, including weather data mining, environmental machine learning, functional hydrology, and numerical forecasting [6]. The problem was solved using a combination of AI, ML, and Deep Learning.…”

Section: Related Workmentioning

confidence: 99%

generalized and finely tunable rainfall prediction based on optimized forecasting model

Minu

Aaman

Reddy

et al. 2022

ijhs

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Agriculture is the most important factor in India for survival. Rainfall is the most critical factor for agriculture. A new approach for designing the network structure in a deep neural network (DNN) based forecasting model for rainfall prediction is presented. The number of atmospheric parameters determines whether the rain will fall. We suggest a systematic approach for analysing various parameters affecting rainfall in the atmosphere in addition to identifying seasonal and diurnal variables, the ground-based weather features Temperature, Relative Humidity, Dew Point, Solar Radiation, Precipitable Water Vapor (PWV) are also identified, and a detailed feature correlation study is presented. Forecasting rainfall in non-monsoon sessions required input values of temperature, wind velocity, humidity, and cloud cover. Among the parameters used to evaluate results were MSE (mean square error), CCI (correlation coefficient), COE (coefficient of efficiency), and MAE (mean absolute error). By using the proposed approach, the proposed scheme establishes that deep learning (DL) can be effective in improving rainfall prediction quality, exploiting the potential of neural network (NN) in using a weather simulator. In terms of daily precipitation characteristics, this various model is applied to 12 different sites under various climatic reinforcements.

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“…The results unveiled the significant environmental variables bearing a positive or negative impact on rainfall and predicted the daily rainfall using these variables. Grace and Suganya (2020) discussed the significance of rainfall prediction and its impact on a country's productivity, including irrigation, water supply, and property damage, and focused on proposing an improved model for accurate rainfall forecasting in Udupi district, Karnataka, India. Sani et al (2020) conducted research using a Malaysian dataset with an ensemble learning approach to increase the accuracy of rainfall forecasts.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction

Abdullah,

said

2023

Preprint

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Accurate prediction of rainfall has always been the most demanding task involved in weather forecasting in view of significant variations in weather patterns. With the advent of machine learning algorithms, it is now possible to predict rainfall with higher precision by extracting hidden patterns from the past hydrometeorological data. However, it can be challenging to select a suitable algorithm for the prediction of daily, monthly, or annual rainfall estimates. In this study, three data-driven machine learning (ML) regression models; Random Forest Regression (RFR), Support Vector Regression (SVR), and CatBoost Regression (CBR) were applied to predict daily and monthly rainfall for Aligarh District, Uttar Pradesh, India. Weather datasets from 1980 to 2020 were utilized, that included maximum and minimum temperature, dew point, relative humidity, wind speed, cloud cover as input variables and rainfall as the target. Results revealed that CBR surpassed RFR and SVR in both daily and monthly rainfall predictions. The CBR and RFR models predicted daily rainfall with a moderate correlation, while the SVR model could not predict rainfall on daily timescale data. All three ML models predicted monthly rainfall with strong correlations, with the CBR exhibiting the strongest. The study concluded that the CBR can be effectively utilized for time series hydrological analysis, and the model can serve as a basis for potential comparisons and recommendations.

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Machine Learning based Rainfall Prediction

Cited by 30 publications

References 8 publications

Tenki Weather Prediction Model

Tenki Weather Prediction Model

generalized and finely tunable rainfall prediction based on optimized forecasting model

Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction

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