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Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critic...
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... other words, the EK-stars method increases the impact of strong training instances to prevent mislearning so that the probability of misclassification is reduced. Figure 1 shows the general overview of the rainfall prediction system which uses the proposed EK-stars method. First, in the data acquisition step, the meteorological data (temperature, rainfall, evaporation, sunshine, wind, humidity, pressure, and cloud) were obtained from the observation stations, leading to the generation of big data. ...
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... the system powered by the proposed machine learning model could detect the rainfall status in advance, human risks and environmental risks could be prevented without serious consequences by serving the "Good Health and Well-Being" and "Life on Land" purposes. Sustainability 2023, 15, x FOR PEER REVIEW 7 of 25 Figure 1. The sustainable rainfall prediction system using the proposed EK-stars approach. ...
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... Among these models, XGBoost demonstrated superior performance, attributed to its capability to handle complex relationships between variables. Another approach, an ensemble of K-stars (EK-stars), was proposed for next-day rainfall prediction using meteorological data from Australia [5]. This study introduced a probability-based aggregating (pagging) approach, surpassing the original K-star algorithm and other recent studies in terms of classification accuracy. ...
Forecasting rainfall, crucial for agriculture, water management, and disaster preparedness, presents significant challenges due to intricate relationships often missed by conventional statistical methods. Hence, machine learning (ML) models offer promising alternatives, enhancing the precision and dependability of rainfall predictions. This paper presents a comprehensive comparison of various ML models with diverse model structures and regularization strategies for rainfall prediction in urban metropolitan cities. The results show that the random forest model, and gradient boosting model outperform the other models such as logistic regression, support vector machine (SVM), decision tree, K-nearest neighbor (KNN), Naive Bayes, linear SVM, and neural network in terms of accuracy. Validation accuracies of 75%, 77%, 68%, 78%, 76%, 78%, 74%, 75% and 76% were achieved for logistic regression, SVM, decision tree, random forest model, KNN, gradient boosting model, Naive Bayes, linear SVM, and neural network, respectively. The choice of ML models for rainfall prediction should consider the characteristics of the data, e.g. a lag feature for 20 days was employed that uses previous time steps to predict the next time step. The paper concludes that ML models, especially the random forest and gradient boosting models are powerful and robust tools for rainfall prediction in urban metropolitan cities. Key Words: rainfall prediction, wind information, machine learning, gradient boosting, random forest
... The study area's best accurate forecasts were produced by the hybrid model, which included six membership functions. The goal of the hybrid model created by Jayasree et al. [44] was to improve the accuracy of yearly rainfall predictions by combining RF with Empirical Mode Decomposition (EMD). The rainfall signal was broken down into six Intrinsic Mode Functions (IMFs) using the EMD approach in order to reveal hidden patterns. ...
Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.
Rainfall prediction is a beneficiary one, but it is a challenging task. Machine learning techniques can use computational methods and predict rainfall by retrieving and integrating the hidden knowledge from the linear and non-linear patterns of past weather data. This paper investigates the effectiveness of two machine learning algorithms - Logistic Regression (LR) and Random Forest (RF) - for rainfall prediction using historical datasets. This study would assist researchers in analysing the most recent work on rainfall prediction with an emphasis on machine learning techniques and providing a reference for possible guidance and comparisons. Anaconda framework is used, and the coding language used is Python, which is portable and dynamic. NumPy, matplotlib, seaborn, and pandas are the libraries used for the implementation. The main objective of this paper is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. This project focuses on harnessing the power of basic machine learning algorithms to unravel insights from a historical rainfall dataset. Insights are interpreted through feature importance analysis and visualization of decision tree structures. In essence, this project showcases the efficiency of basic machine learning algorithms in uncovering hidden information within a rainfall dataset.
