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Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation
(2024-06-01)
Jitkajornwanich, Kulsawasd
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Vijaranakul, Nattadet
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Jaiyen, Saichon
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Srestasathiern, Panu
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Lawawirojwong, Siam
Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well. During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI prediction based on satellite images. Our hybrid model consists of two parts as follows: • The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation. • The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI. From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R<sup>2</sup> (R<sup>2</sup> > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.
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Visualizing Political Communication Trends across Generations on X (Twitter): Insights Through Topic Modeling and Word Clouds
(2024-01-01)
Udomwisanpat, Prinwat
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Jitkajornwanich, Kulsawasd
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Kraishan, Obada
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Srestasatheirn, Panu
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Lawawirojwong, Siam
This study examines the interests and significance of words on Twitter (or X) across different generational groups: Baby Boomers, Generation X, Generation Y, and Generation Z. Using Topic Modeling with Latent Dirichlet Allocation (LDA), the research explores relationships and word importance within each group. As the results of topic modeling are not always easy to interpret, we used word cloud visualization to help make sense of the results for each generation. The findings reveal distinct patterns: Baby Boomers frequently mention print media, news websites, and prominent Thai political figures; Generation X emphasizes individuals and local political issues in Bangkok; Generation Y discusses political and social events; and Generation Z uniquely questions political and social activities. This research methodology is applicable across languages and tasks, offering insights into generational behaviors and interests.
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Transformer-based decoder designs for semantic segmentation on remotely sensed images
(2021-12-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.
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Air Quality Assessment Based on Landsat 8 Images Using Supervised Machine Learning Techniques
(2020-06-01)
Vijaranakul, Nattadet
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Jaiyen, Saichon
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Srestasathiern, Panu
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Lawawirojwong, Siam
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Jitkajornwanich, Kulsawasd
Since 2018 during the winter of every year (December - January), Thailand has been suffering from air pollution problems known as PM 2.5 toxic dust, affecting people's daily lives especially in Bangkok and its metroplex. To cope with this problem, one of the traditional methods used is to implement physical air quality measurement devices at specific locations. Currently there are 21 stations across Bangkok and surrounding areas. Each station can assess air quality at the station point along with the given radius, meaning that areas far away from the station will not be assessed properly. In this paper, we propose a methodology that incorporates satellite images for air quality assessment with supervised machine learning techniques. Several classification models tested in this paper are Decision Tree, Naïve Bayes, k-Nearest Neighbors (kNN), Random Forest, and Gradient Boosting. From our experiments, the highest performance model is Random Forest that has averaged accuracy of 0.914, averaged precision of 0.89, averaged recall of 0.814 and averaged F-1 score of 0.84825.
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Semantic labeling in remote sensing corpora using feature fusion-based enhanced global convolutional network with high-resolution representations and depthwise atrous convolution
(2020-04-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
One of the fundamental tasks in remote sensing is the semantic segmentation on the aerial and satellite images. It plays a vital role in applications, such as agriculture planning, map updates, route optimization, and navigation. The state-of-the-art model is the Enhanced Global Convolutional Network (GCN152-TL-A) from our previous work. It composes two main components: (i) the backbone network to extract features and (ii) the segmentation network to annotate labels. However, the accuracy can be further improved, since the deep learning network is not designed for recovering low-level features (e.g., river, low vegetation). In this paper, we aim to improve the semantic segmentation network in three aspects, designed explicitly for the remotely sensed domain. First, we propose to employ a modern backbone network called "High-Resolution Representation (HR)" to extract features with higher quality. It repeatedly fuses the representations generated by the high-to-low subnetworks with the restoration of the low-resolution representations to the same depth and level. Second, "Feature Fusion (FF)" is added to our network to capture low-level features (e.g., lines, dots, or gradient orientation). It fuses between the features from the backbone and the segmentation models, which helps to prevent the loss of these low-level features. Finally, "Depthwise Atrous Convolution (DA)" is introduced to refine the extracted features by using four multi-resolution layers in collaboration with a dilated convolution strategy. The experiment was conducted on three data sets: two private corpora from Landsat-8 satellite and one public benchmark from the "ISPRS Vaihingen" challenge. There are two baseline models: the Deep Encoder-Decoder Network (DCED) and our previous model. The results show that the proposed model significantly outperforms all baselines. It is the winner in all data sets and exceeds more than 90% of F1: 0.9114, 0.9362, and 0.9111 in two Landsat-8 and ISPRS Vaihingen data sets, respectively. Furthermore, it achieves an accuracy beyond 90% on almost all classes.
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The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data
(2020-01-01)
Pongto, Ratchanont
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Wiwattanaphon, Nopparat
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Lekpong, Peerapon
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Lawawirojwong, Siam
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Srisonphan, Siwapon
Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7Â h prior. This configuration was found to be the best combination.
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Combining attentional cnn and gru networks for ocean current prediction based on hf radar observations
(2019-10-23)
Thongniran, Nathachai
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
Lately, CNN-GRU demonstrates the ability of deep learning techniques on ocean surface current prediction. Improvement of the current prediction model creates positive impact on variety of marine activities, such as search-and-rescue, disaster monitoring and power forecasting. Deep learning techniques was successfully deployed to improve model performance in many areas due to their ability to handle enormous amounts of information in a variety of inputs and their huge growth in recent years. Latest ocean current prediction employed a combination of two mature techniques, which are Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), to capture spatial and temporal characteristics of its nature. However, there is still room for improvement due to many modern techniques that still have not been employed, and domain knowledge in oceanic, such as lunar illumination, is not taken into account to improve prediction performance. This paper introduces the ocean surface prediction model that employs soft attention mechanism, transfer learning, and incorporation of domain knowledge inputs which are month number, lunar effect, and hour number. An experimental dataset from 2014 to 2016, provided by GISTDA, is collected by using high frequency (HF) radar stations located along the coastal Gulf of Thailand. The experiment compares an existing CNN-GRU and our proposed model. The result shows an improvement of the prediction model in terms of RMSE by 2.57%, and 3.44% on U and V components.
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Spatio-Temporal deep learning for ocean current prediction based on hf radar data
(2019-07-01)
Thongniran, Nathachai
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Vateekul, Peerapon
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
Ocean surface current prediction is necessary to carry a variety of marine activities, such as disaster monitoring, search and rescue operations, etc. There are three traditional forecasting approaches: (i) numerical based approach, (ii) time series based approach and (iii) machine learning based approach. Unfortunately, their prediction accuracy was limited since they did not cooperate with spatial and temporal effects together. In this paper, we present a novel current prediction model, which is a combination between Convolutional Neural Network (CNN) to extract spatial characteristic and Gated Recurrent Unit (GRU) to find a relationship of temporal characteristic. The dataset is collected by high frequency (HF) radar station's located along coastal Thailand's gulf by GISTDA from 2014 to 2016. It was an intensive experiment comparing our method and eight existing methods, e.g., ARIMA, kNN, Perceptron, Multilayer Perceptron (MLP), etc. The results show that our network outperforms almost all baselines in terms of RMSE for 11.21% and 27.01% averaging improvement on U and V components, consecutively.
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Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning
(2019-01-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc., on raster images. A deep convolutional encoder-decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, "channel attention" is presented in our network in order to select the most discriminative filters (features). Third, "domain-specific transfer learning" is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of F1 for 17.48% and 2.49% on medium and very high resolution corpora, respectively.
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Utilizing Twitter Data for Early Flood Warning in Thailand
(2018-07-02)
Jitkajornwanich, Kulsawasd
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Kongthong, Chanwit
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Khongsoontornjaroen, Nattaya
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Kaiyasuan, Jeedapa
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Lawawirojwong, Siam
Natural disasters cause significant damage to the country as well as its citizens as we have seen in the news. Drought, wild fire, earthquake and flooding are some examples of the primary natural disasters occurred in Thailand. In this research, we focus on »flooding» and use data from Twitter, where users' mobile devices are utilized as IoT input channels. The goal of this work is to analyze near real-time data (tweets) for early flood warning. Traditional methods in processing, analyzing and reporting a flooding event take quite some time. In social medias (through cellphones), on the other hand, by harvesting crowdsources, potential flooding can be predicted faster - though with the price of reliability of the retrieved tweets. In our research, several techniques are incorporated in order to maximize the accuracy of results, including, tokenization, geo-encoding and decoding, NLP via string matching (Levenshtein's algorithms), and Google APIs for visualization. Finally, the dynamic yet user-friendly map is produced with respect to the posted relevant tweets along their associated frequencies.

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