Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

Author(s):  
Sudhan Murugan Bhagavathi ◽  
Anitha Thavasimuthu ◽  
Aruna Murugesan ◽  
Charlyn Pushpa Latha George Rajendran ◽  
Vijay A ◽  
...  

This project proposes a method for forecasting weather conditions and predicting rainfall by means of machine learning. Here, there are two set ups: one, to measure the weather parameters like temperature, humidity using sensors along with Arduino and another set up, to display the current values(status) and predicted rainfall based on the trained machine learning data sets. The weather forecasting and prediction is done based on the older datasets collected and compared with the current values. The user need not have a backup of huge data to predict the rainfall. Instead a machine learning algorithm can suffice the same. The temperature, humidity sensor modules are used to measure weather parameters and interfaced to an Arduino controller. The proposed setup will compare the forecast value with real-time data, and the predict rainfall based on the dataset fed to the machine learning algorithm.


2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.


2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.


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