scholarly journals Predicting Rainfall using Machine Learning Techniques

2021 ◽  
Author(s):  
Nikhil Oswal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Machine Learning Techniques ◽  
Evaluation Metrics ◽  
Weather Data ◽  
Human Society ◽  
Rainfall Prediction ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Processing Techniques

<p>Rainfall prediction is one of the challenging and uncertain tasks which has a signicant impact on human society. Timely and accurate predictions can help to proactively reduce human and nancial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.</p>

scholarly journals Predicting Rainfall using Machine Learning Techniques

2021 ◽  
Author(s):  
Nikhil Oswal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Machine Learning Techniques ◽  
Evaluation Metrics ◽  
Weather Data ◽  
Human Society ◽  
Rainfall Prediction ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Processing Techniques

<p>Rainfall prediction is one of the challenging and uncertain tasks which has a signicant impact on human society. Timely and accurate predictions can help to proactively reduce human and nancial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.</p>

scholarly journals Machine Learning Technique for Rainfall Prediction

2021 ◽  
Vol 9 (VI) ◽  
pp. 594-600
Author(s):  
Dr. Vivek Waghmare
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Real Data ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Human Society ◽  
Rainfall Prediction ◽  
Reliability Estimates ◽  
Machine Learning Model

Rain prediction is one of the most challenging and uncertain tasks that has a profound effect on human society. Timely and accurate forecasting can help significantly reduce population and financial losses. This study presents a collection of tests involving the use of conventional machine learning techniques to create rainfall prediction models depending on the weather information of the area. This Comparative research was conducted focusing on three aspects: modeling inputs, modeling methods, and prioritization techniques. The results provide a comparison of the various test metrics for these machine learning methods and their reliability estimates in rain by analyzing weather data. This study seeks a unique and effective machine learning system for predicting rainfall. The study experimented with different parameters of the rainfall from various regions in order to assess the efficiency and durability of the model. The machine learning model is focused on this study. Rainfall patterns in this study are collected, trained and tested for achievement of sustainable outcomes using machine learning models. The monthly rainfall predictions obtained after training and testing are then compared to real data to ensure the accuracy of the model The results of this study indicate that the model has been successful in it predicting monthly rain data and specific parameters.

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

2020 ◽  
pp. 1096-1117
Author(s):  
Rodrigo Ibañez ◽  
Alvaro Soria ◽  
Alfredo Raul Teyseyre ◽  
Luis Berdun ◽  
Marcelo Ricardo Campo
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Gesture Recognition ◽  
Microsoft Kinect ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Natural Interaction ◽  
Computer Communication ◽  
Human Computer Interfaces ◽  
Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

scholarly journals A Comparative Study of Machine Learning Techniques to Simulate Land Use Changes

2018 ◽  
Vol 47 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Mohammad Ahmadlou ◽  
Mahmoud Reza Delavar ◽  
Anahid Basiri ◽  
Mohammad Karimi
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Comparative Study ◽  
Land Use Changes ◽  
Machine Learning Techniques ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document