Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang, Ziqi; Chen, Ying-Chih; Wen, Chih-Yu

doi:10.3390/s20185173

Cited by 38 publications

(13 citation statements)

References 19 publications

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“…Cite Machine learning method Machine learning type [72] Poisson mixture model Unsupervised [69] K-means clustering, decision tree Supervised, unsupervised [34] Decision tree Supervised [56] SVM, k-NN, elasticnet, random forest Unsupervised [55] Mixed-variate restricted Boltzman machine Unsupervised [7] Ada boost Supervised [79] Multi-layered perceptron, linear regression Supervised, unsupervised [102] Growing HMM Supervised [82] Kernel density estimation Unsupervised [84] Logistic regression Unsupervised [70] Latent Dirichlet allocation Unsupervised [49] Long short-term memory, multi-layer perceptron Supervised [11] Artificial neural network Supervised [98] SVM, k-NN, ridge regression Supervised [71] SVM Supervised [15] Long short-term memory Supervised [91] CatBoost, XGBoost, light gradient boosting machine Supervised [96] Naive Bayes, logistic regression, SVM Unsupervised…”

Section: Conclusion and Open Challengesmentioning

confidence: 99%

Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning is used to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design.

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Section: Conclusion and Open Challengesmentioning

confidence: 99%

Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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“…The author's aim is to obtain live information on weather conditions on OLED display. The author in [4], proposed a system that monitors and predicts the weather condition by which anyone can plan for our day-to-day life. This activity became helpful in every field either in agriculture or industry.…”

Section: Literature Survey-mentioning

confidence: 99%

Real Time Weather Monitoring System Using Iot

Sharma¹,

Prakash

2021

ITM Web Conf.

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In Today’s World, knowing live environmental condition is one of the biggest issues because there is an IoT of hurdles arrives when live environmental condition is measured. The proposed system will remove this problem since it monitors real-time weather conditions. In this proposed work we will monitor the live weather’s parameter of the Gorakhpur Region. The proposed system will work on the client-server architecture model using IoT. The system is organized in Two-tier Architecture. Our proposed system contains a various sensor which will monitor the temperature of the region, humidity, Rain value and pressure of the system. The sensor captured data and send it to the node MCU controller. Arduino ide is used to upload the sensed data. The serial monitor has worked as a gateway between the sensor and the cloud. The data is pushed by the sensor on a serial monitor. The serial monitors an IP address. The HTTP protocol is used to view the data on the webserver. This paper displays the data on the webserver and monitor the real-time data of weather using environmental parameter or sensor. Using a webserver, everyone can monitor the weather’s condition from anywhere without depending on any application or website. The data is available publicly. With the help of this proposed system, we measure the weather condition of the Gorakhpur Region. After getting results from the various sensor, it is observed that our proposed model achieves better results in comparison with the standard weather parameter.

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“…The supervised ML algorithms fall under the regression category are capable of predicting a result based on the previously provided data set and the obtained results from them. Weather forecasting [80], stock market forecasting [81], population growth forecasting, products recommendations [70], and fraud detection [71] are the most popular applications of supervised regression algorithms. Due to the advantages of regression algorithms in predictive analysis, several researchers have used regression algorithms such as linear regression, non-linear regression, multi-linear regression, to forecast the spread pattern of corona disease.…”

Section: ) Regression Modelmentioning

confidence: 99%

Intelligent COVID-19 Forecasting, Diagnoses and Monitoring Systems: A Survey

Anjum¹,

Asif²,

Kiran³

et al. 2021

Preprint

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<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>

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Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Cited by 38 publications

References 19 publications

Survey of Machine Learning Methods Applied to Urban Mobility

Survey of Machine Learning Methods Applied to Urban Mobility

Real Time Weather Monitoring System Using Iot

Intelligent COVID-19 Forecasting, Diagnoses and Monitoring Systems: A Survey

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