Untangling the contribution of input parameters to an artificial intelligence PM2.5 forecast model using the layer-wise relevance propagation method

Kim, Dasol; Ho, Chin Siong; Park, Ingyu; Kim, Jinwon; Chang, Lim‐Seok; Choi, Min-Hyeok

doi:10.1016/j.atmosenv.2022.119034

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…It is confirmed that the forecasting of the RNN model chiefly depends on the input information. The MAE of the RNN model for PM 2.5 prediction is 8.4 [ 59 ]. The optimized LSTM model has good assessment criteria, with R 2 = 0.94, RMSE = 13.06 μg/m 3 , and MAE = 8.61 μg/m 3 [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Guo

Wang

2023

Toxics

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Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

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Section: Resultsmentioning

confidence: 99%

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Guo

Wang

2023

Toxics

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“…Further, this LRP extension to recurrent networks was successfully applied in the health domain for therapy prediction 60 and in computer security for discovering vulnerability in source code 61 . A recent work also applied the technique to a recurrent network for forecasting, where it helped identifying a subset of input variables that lead to a similar prediction performance when the model was re-trained only on this subset 62 .…”

Section: Related Workmentioning

confidence: 99%

Explainable sequence-to-sequence GRU neural network for pollution forecasting

Borujeni

Arras

Srinivasan

et al. 2023

Sci Rep

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The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regression models and mechanistic approaches. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the model’s decision-making process, providing insights into decisive input features responsible for the model’s prediction. One XAI technique to explain the predictions of neural networks which was proven useful in various domains is Layer-wise Relevance Propagation (LRP). In this work, we extend the LRP technique to a sequence-to-sequence neural network model with GRU layers. The explanation heatmaps provided by LRP allow us to identify important meteorological and temporal features responsible for the accumulation of four major pollutants in the air ($$\text {PM}_{10}$$ PM 10 , $$\text {NO}_{2}$$ NO 2 , $$\text {NO}$$ NO , $$\text {O}_{3}$$ O 3 ), and our findings can be backed up with prior knowledge in environmental and pollution research. This illustrates the appropriateness of XAI for understanding pollution forecastings and opens up new avenues for controlling and mitigating the pollutants’ load in the air.

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“…Due to the buildup of air pollutants brought into Indonesia from continental sources to the west of Indonesia, record-breaking PM2.5 concentrations (peak >150 µgram/m 3 ) were detected in Tangerang in July-October 2022. For high PM2.5 events (PM2.5 36 µgram/m 3 ) in 2021, NIER's one-day prediction accuracy was only 56% [3], [4], [5].…”

Section: Levels In Recognition Of the Severely Harmful Impactsmentioning

confidence: 99%

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Rahardja¹,

Aini²,

Sunarya³

et al. 2022

att

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Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.

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Untangling the contribution of input parameters to an artificial intelligence PM2.5 forecast model using the layer-wise relevance propagation method

Cited by 14 publications

References 23 publications

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Explainable sequence-to-sequence GRU neural network for pollution forecasting

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

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