Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in advance regarding: ongoing construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research. This review will serve the researchers to analyze the latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.

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Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review


 It is crucial to predict the amount of daily rainfall to improve agricultural productivities to secure food, and water quality supply to keep the citizen healthy. To predict rainfall, various researches are conducted using data mining and machine learning techniques of different countries’ environmental datasets. The Pearson correlation technique is used to select relevant environmental variables which are used as an input for the machine learning model of this study. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The dataset is collected from the local meteorological office to measure the performance of three machine learning techniques as Multivariate Linear Regression, Random Forest and Extreme Gradient Boost. Root mean squared error and Mean absolute Error are used to measure the performance of the machine learning model for this study. The result of the study shows that the Extreme Gradient Boost gradient descent machine learning algorithm performs better than others.

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Machine Learning Techniques to Predict Daily Rainfall Amount

Rainfall is one of the most vital components of agriculture and also predicting it is the most challenging task. In general, weather and rainfall are highly non-linear and complex phenomena, which require progressive computer modeling and simulation for their precise prediction. Numerous and diverse machine learning models are used to predict the rainfall which are Multiple Linear Regression, Neural networks, K-means, Naïve Bayes and more. These systems implement one of these applications by extracting, training and testing data sets and finding and predicting the rainfall. This study shows the using of Multiple Linear Regression and Neural networks to predict rainfall and Decision Trees algorithm to recommend crops. Thus, we inferred that we can predict the rainfall and recommend crops with reasonable accuracy. Keywords— Neural networks, Linear Regression, Decision Tree, Rainfall, Crop Recommendation, Machine Learning.

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Rainfall Prediction using Linear approach & Neural Networks and Crop Recommendation based on Decision Tree

The whole world facing a huge crisis because of Corona virus also known as COVID-2019, identified first in December 2019 in the city of Wuhan located in China The detection of persons infected with the virus is most important as it can be spread easily from him to others and also the person infected with the virus may not know that he is infected until a number of symptoms fallout from him In this paper the virus detection is done using deep learning and machine learning algorithms using the X-ray images A dataset is created with three classes consisting of normal, corona virus, and pneumonia images The proposed method uses ResNet50 and SVM, deep learning features are extracted using ResNet50 and classification is done using SVM classifier The classification accuracy obtained from the model is 100% when testing on the Corona virus and normal images, whereas the results obtained from the model is 94% when it is tested on the dataset consisting of normal, Corona virus and pneumonia images and performed well compared to VGG16 © 2020 IOP Conference Series: Materials Science and Engineering

https://iopscience.iop.org/article/10.1088/1757-899X/993/1/012046/pdf

Detection of COVID-19 using Hybrid ResNet and SVM

In a country, economic growth indicators are measured by GDP growth rates. In order to predict GDP growth then, economic situation and economic development strategy have always been used. In this study, GDP contribution at the current price by the industrial sector has been predicted by using heuristic network method. There were nine GDP growth variables including (1) Agriculture, Livestock, Forestry, Fishery, (2) Mining & Quarrying, (3) Manufacturing Industry, (4) Electricity, Gas, Water supply, (5) Construction, (6) Trade, Hotel, Restaurant, (7) Transport& Communication, (8) Finance, Real Estate & Business Services, and (9) Services in the period 2001–2016 have been analyzed. Experimental results show that the error rate forecasting in 2017 is less than 10%. The results show that intelligent computing method (heuristic network) can be an alternative method for predicting the contribution of GDP. This method predicts fairly quickly, significantly and produces an acceptable error prediction.

A Heuristic Network for Predicting the Percentage of Gross Domestic Product Distribution

This paper is carried on the heuristic prediction of rainfall using machine learning techniques. As we know agriculture was the predominant of our country and economy. While a regular rain pattern is usually played vital for healthy agriculture but too much rainfall or too little rainfall can be harmful, even it led to devastating of crops. This paper discusses the rate of rainfall in previous years according to various crops seasons like rabi, Kharif, zaid and predicts the rainfall in future seasons. The paper also measures the different categories of data by linear regression method in metrics for effective understanding of agriculture in India. We have selected a real dataset which consists of past year's rainfall rate according to various seasons. Results of this application help farmers to make a correct decision to harvest a particular crop accordingly to crops seasons. Linear regression helps to find

K Chaitanya Krishna

Papers

Heuristic prediction of rainfall using machine learning techniques