Transboundary Smog Pollution Damage Prediction

Overview

Purpose
cross-data analysis
Data loader
Applications
Configuration
Operating environment
Precautions
Reference

Purpose

This information asset is derived from the environmental quality short-term prediction^[1]and performs the process of learning and prediction PM10 concentration (Haze) using cross-data analysis^[2] from acquired environmental observation data.

cross-data analysis

PM10 concentration (Haze) prediction is based on a multimodal model. The multimodal model uses Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), a type of Recurrent Neural Networks (RNN), to predict features of weather data. This multimodal model is used to predict PM10 (Haze) concentration within the target area.

Data loader

The pre-acquired environmental observation data used in the prediction of smog transboundary pollution damage is extracted and registered in a database for analysis in event data format.

Applications

Research on transboundary pollution damage
Government public health crisis management, etc.

Configuration

This information asset consists of.

Processing program（Python）
- Programs for selecting the area to be measured
- There are similar programs in each of Brunei, Indonesia, Thailand, and Singapore.
  - Multimodal model: one region targeted study program
  - Multimodal model: one region targeted test program
  - Multimodal Model: Learning programs for Multiple Regions
  - Multimodal model: testing programs targeting multiple regions
  - Multimodal model: Learning programs targeting a country
  - Multimodal model: Testing programs targeting a country
- PM10 concentration (Haze) prediction model: prediction program for multiple regions
- PM10 concentration (Haze) prediction model: cross-country prediction program
Data model（public format）
There are similar programs in each of Brunei, Indonesia, Thailand, and Singapore.
- Multimodal model: one region targeted study table
- Multimodal model: one region targeted test table
- Multimodal Model: Learning table for Multiple Regions
- Multimodal model: testing table targeting multiple regions
- Multimodal model: Learning table targeting a country
- Multimodal model: Testing table targeting a country
Prediction model（multimodal method.）
- PM10 concentration (Haze) prediction model

Operating environment

We confirmed the operation in the following environment.

OS : Ubuntu 18.04 LTS
RAM : 4GB Recommended
DISK SPACE : 100GB or more
Internet connection available
Python : 3.x（Does not work with 2.x）
- Python library
PostgreSQL : 9.2 or more

When using the xDataEdge^[3]environment, the following conditions are required in addition to the above.

Services such as Apache, Nginx, etc. that have HTTP server functions (LISTEN to HTTP/HTTPS ports) must not be running on the OS.
Have root privileges on the OS where xData Edge is installed

Precautions

Restrictions
- Functional Limitations
  - This information asset is not guaranteed to operate in environments other than those shown in the operating environment.
- Data Restrictions
  - There are restrictions on the data to be registered in the database. For details, please refer to the Data Loader API Manual^[4].
Disclaimer
- While every effort has been made to ensure that the information on this page is as accurate as possible, we do not guarantee its accuracy or safety.
- We are not responsible for any damages caused by the contents of this page or by what is provided.
- All information provided on this page is sample only and we are not responsible for any predicted results.

Reference

[1]Please refer to the catalog page for information assets for short-term environmental quality prediction here.
[2]Asem Kasem, Minh-Son Dao, et.al.: "Overview of MediaEval 2021: Insights for Wellbeing TaskCross-Data Analytics for Transboundary Haze Prediction, MediaEval 2021".
[3]xData Edge manual : https://www.xdata.nict.jp/docs/Edge/1.0/.
[4]Data Loader API manual : https://www.xdata.nict.jp/docs/DataLoader/0.6/.