scholarly journals Weather Prediction using Machine Learning and IOT

2020 ◽  
Vol 9 (4) ◽  
pp. 2094-2098
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Weather Parameters ◽  
Set Up

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.

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3139 ◽  
Author(s):  
Félix Hernández-del-Olmo ◽  
Elena Gaudioso ◽  
Natividad Duro ◽  
Raquel Dormido
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Weather Forecasting ◽  
Wastewater Treatment Plants ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Soft Sensor ◽  
Physical Sensors ◽  
Useful Signal

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

Stuck Pipe Early Detection on Extended Reach Wells Using Ensemble Method of Machine Learning

2021 ◽  
Author(s):  
Rushad Ravilievich Rakhimov ◽  
Oleg Valerievich Zhdaneev ◽  
Konstantin Nikolaevich Frolov ◽  
Maxim Pavlovich Babich
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Method ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Machine Learning Model ◽  
Real Time Data ◽  
The Ensemble Method

Abstract The ultimate objective of this paper is to describe the experience of using a machine learning model prepared by the ensemble method to prevent stuck pipe events during well construction process on extended reach wells. The tasks performed include collecting, analyzing and cleaning historical data, selecting and preparing a machine learning model, testing it on real-time data by means of desktop application. The idea is to display the solution at the rig floor, allowing Driller to quickly take actions for prevention of stuck pipe event. Historical data mining and analysis were performed using software for remote monitoring. Preparation, labelling and cleaning of historical and real-time data were executed using programmable scripts and big data techniques. The machine learning algorithm was developed using the ensemble method, which allows to combine several models to improve the final result. On the field of interest, the most common type of stuck pipe are solids induced pack offs. They occur due to insufficient hole cleaning from drilled cuttings and wellbore collapse due to rocks instability. Stuck pipe prevention on extended reach drilling (ERD) wells requires holistic approach meanwhile final role is assigned to the driller. Due to continuously exceeding ERD envelope and increased workloads on both personnel and drilling equipment, the effectiveness of preventing accidents is deteriorating. This leads to severe consequences: Bottom Hole Assembly lost in hole, the necessity to re-drill the bore and eventually to increased Non-Productive Time (NPT). Developed application based on ensemble machine learning algorithm shows prediction accuracy above 94%. Reacting on alarms, driller can quickly take measures to prevent downhole accidents during well construction of ERD wells.

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

2021 ◽  
Author(s):  
Sudhan Murugan Bhagavathi ◽  
Anitha Thavasimuthu ◽  
Aruna Murugesan ◽  
Charlyn Pushpa Latha George Rajendran ◽  
Vijay A ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals Investigation of efficiency of application of machine learning algorithm for classification of internet traffic

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
A. P. Kozyryatsʹkyy ◽  
◽  
V. V. Zhebka ◽  
L. O. Dʹomina ◽  
D. O. Tarasenko
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Access Point ◽  
Test Sample ◽  
Internet Traffic ◽  
Training Sample ◽  
Machine Learning Algorithm ◽  
Class Composition ◽  
Set Up

The article investigates the effectiveness of the machine learning algorithm for the classification of Internet traffic. The RF algorithm, which works by constructing many decision trees, is considered. The efficiency of the RF algorithm in the problems of application classification in the presence and absence of background network traffic is evaluated. A laboratory network of several computers was set up to collect the data needed for analysis. One of the computers was connected to the World Wide Web and a wireless access point was set up on its base. On the same computer, all the traffic passing through it was captured using Wireshark. Various applications were running on other computers connected to the access point. Web pages were viewed using Google Chrome and Opera browsers, using Skype, video calls were made, files were downloaded using the µTorrent torrent client, the Steam digital game distribution service was used, etc. The obtained data were stored in the PCAP format. To bring the obtained data in line with the requirements of the problem, the data was pre-processed. In the experiment, a random forest was constructed and the quality of classification on a given sample was assessed. The most acceptable parameters of the algorithm were selected experimentally. It is experimentally chosen that the forest consists of 5 trees with the maximum possible depth. The algorithm is most effective for data related to DNS traffic. In addition to checking the operation of the algorithm on the test sample, which has the same class composition as the training, the assessment of its quality was also carried out in the presence of background traffic, i.e. in the test sample there were copies of classes absent in the training sample.

scholarly journals Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Bin Ye ◽  
Kangping Liu ◽  
Siting Cao ◽  
Padmaja Sankaridurg ◽  
Wayne Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Segmentation Method ◽  
Time Data ◽  
Threshold Segmentation ◽  
Svm Model ◽  
Real Time Data

Abstract Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

Machine Learning Predictions as Regression Covariates

2020 ◽  
pp. 1-18
Author(s):  
Christian Fong ◽  
Matthew Tyler
Keyword(s):  
Machine Learning ◽  
Prediction Error ◽  
Learning Algorithm ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Regression Analyses ◽  
Political Dialogue ◽  
Latent Features ◽  
Text Images ◽  
True Values

Abstract In text, images, merged surveys, voter files, and elsewhere, data sets are often missing important covariates, either because they are latent features of observations (such as sentiment in text) or because they are not collected (such as race in voter files). One promising approach for coping with this missing data is to find the true values of the missing covariates for a subset of the observations and then train a machine learning algorithm to predict the values of those covariates for the rest. However, plugging in these predictions without regard for prediction error renders regression analyses biased, inconsistent, and overconfident. We characterize the severity of the problem posed by prediction error, describe a procedure to avoid these inconsistencies under comparatively general assumptions, and demonstrate the performance of our estimators through simulations and a study of hostile political dialogue on the Internet. We provide software implementing our approach.

Sign in / Sign up

Export Citation Format

Share Document