Subongkod, Mallika
Loading...
Preferred name
Subongkod, Mallika
Main Affiliation
Email
mallika.su@kmitl.ac.th
2 results
Now showing 1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, A study on tourism mobile web application based on big data analysis platform for the South of Thailand(2018-07-02); ; In this paper, we propose to a study on tourism dashboard on mobile web application by approaching based on big data analysis platform for tourism data in southern of Thailand. A case study of this paper is Chumphon province that is a gateway to southern of Thailand. To satisfy and convenient to support tourism information, our design dashboard information can useful for the tourist to plan their traveling as easily. The proposed system consists of data tourism, data storage, data processing, data visualization, and user interface (UI). Note that the proposed system is based on big data analysis platform. Therefore, this proposed system of tourism dashboard on mobile web application can very useful to promote the tourists of the south of Thailand as a growing fast. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring(2022-08-01); ; Klubsuwan, KatanyooAir pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO<inf>2</inf>) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), particulate matter (PM<inf>2.5,10</inf>), and sulfur dioxide (SO<inf>2</inf>), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM<inf>2.5,10</inf> and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.