Ensemble Learning for Rainfall Prediction

Sani, Nor Samsiah; Rahman, Abdul Hadi Abd; Adam, Afzan; Shlash, Israa; Aliff, Mohd

doi:10.14569/ijacsa.2020.0111120

Cited by 22 publications

(13 citation statements)

References 29 publications

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“…[4] proposed a method constructed on the Genetic Algorithm that makes use of a dimensionality reduction methodology as well as a Multilayer Perceptron (MLP) for the purpose of performing an analysis that is both dynamic and efficient using real-time data. [3] suggests using ensemble learning to improve the accuracy of forecasting rainfall. [10] applied various type of machine learning models such as Support vector machine, Decision tree, Random Forest, Naïve bayes, and Neural networks to predict the Malaysia country rainfall.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Forecast Rainfall Density by Utilizing Machine Learning Models

Hsu¹,

Sharma²

2023

World Congress on Civil, Structural, and Environmental Engineering

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Organizations can use weather forecasting to help with decision-making when it comes to preventing disasters. Forecasting rain is challenging since weather conditions are always unpredictable in general. The prediction of rainfall uses a variety of methodologies, including statistical, hybrid, and physical approaches. In this research, we have implemented various machine learning models such as Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP) to predict the density of rainfall. This study has used Taiwan Ruiyan rainfall hourly dataset from 1998 to 2018 which contains five features like Air Pressure, Humidity, Temperature, Windspeed, and Wind Direction to predict the rainfall density such as low, medium, and heavy rainfall. The results data in this study are compared using statistical metrics like AUC, accuracy, recall, precision, and F1-score. The Random Forest, and Multi-Layer Perceptron models, had the highest accuracy scores of 0.71, accurately predicting the results. This study offers a comprehensive overview of several methods and their rainfall density predictions. By comparing these models, we can decide which one is best for predicting rainfall. The suggested work is extensively used in a variety of agriculture and civil applications, including hazard prediction, prevention, operational planning, and many more.

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Section: Related Workmentioning

confidence: 99%

“…In recent years, there have been significant advancements made in the field of machine learning (ML). Previous researchers have applied various type of machine learning to predict the rainfall for example Naïve Bayes, support vector machine, artificial neural networks, decision tree, Gradient boosting machine and many mores [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Forecast Rainfall Density by Utilizing Machine Learning Models

Hsu¹,

Sharma²

2023

World Congress on Civil, Structural, and Environmental Engineering

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“…A parameter-tuning experiment was conducted to determine the best parameters of several available options [40,41].…”

Section: Parameter Tuningmentioning

confidence: 99%

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Abdulkareem¹,

Sani²,

Sahran³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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The coronavirus disease 2019 has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 dataset is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases.

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“…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. Ensemble learning integrates different ML classifiers for predicting rainfall.…”

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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Ensemble Learning for Rainfall Prediction

Cited by 22 publications

References 29 publications

Forecast Rainfall Density by Utilizing Machine Learning Models

Forecast Rainfall Density by Utilizing Machine Learning Models

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction

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