Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

Tao, Hai; Jawad, Ali H.; Shather, A.H.; Al-Khafaji, Zainab S.; Rashid, Tarik A.; Ali, Mumtaz; Al‐Ansari, Nadhir; Marhoon, Haydar Abdulameer; Shahid, Shamsuddin; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.envint.2023.107931

Cited by 17 publications

(1 citation statement)

References 81 publications

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“…With the development of big data and artificial intelligence technology, different machine learning models such as LSTM-INFO, ELM-SAMOA, RVM-IMRFO and RVFL-QANA were used in the field of soil erosion [37][38][39]. As a commonly used model, artificial neural networks (ANNs) are universal and can learn complex nonlinear relationships between important input and output factors by approximating a large class of functions with a high degree of accuracy [40,41].…”

Section: Introductionmentioning

confidence: 99%

Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Gao,

Liu,

Zhang

et al. 2023

Atmosphere

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Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM2.5 emissions from soil erosion, an approach based on an artificial neural network (ANN) model which provides a multilayered, fully connected framework that relates input parameters and outcomes was proposed in this study. Owing to the difficulty in obtaining the actual emissions of soil-derived PM10 and PM2.5 over a broad area, the PM10 and PM2.5 simulated results of the ANN model were compared with the published results simulated by the widely used wind erosion prediction system (WEPS) model. The PM10 and PM2.5 emission results, based on the WEPS, agreed well with the field data, with R2 values of 0.93 and 0.97, respectively, indicating the potential for using the WEPS results as a reference for training the ANN model. The calculated r, RMSE and MAE for the results simulated by the WEPS and ANN were 0.78, 3.37 and 2.31 for PM10 and 0.79, 1.40 and 0.91 for PM2.5, respectively, throughout Southern Xinjiang. The uncertainty of the soil-derived PM10 and PM2.5 emissions at a 95% CI was (−66–106%) and (−75–108%), respectively, in 2016. The results indicated that by using parameters that affect soil erodibility, including the soil pH, soil cation exchange capacity, soil organic content, soil calcium carbonate, wind speed, precipitation and elevation as input factors, the ANN model could simulate soil-derived particle emissions in Southern Xinjiang. The results showed that when the study domain was reduced from the entire Southern Xinjiang region to its five administrative divisions, the performance of the ANN improved, producing average correlation coefficients of 0.88 and 0.87, respectively, for PM10 and PM2.5. The performances of the ANN differed by study period, with the best result obtained during the sand period (March to May) followed by the nonheating (June to October) and heating periods (November to February). Wind speed, precipitation and soil calcium carbonate were the predominant input factors affecting particle emissions from wind erosion sources. The results of this study can be used as a reference for the wind erosion prevention and soil conservation plans in Southern Xinjiang.

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Section: Introductionmentioning

confidence: 99%