Optimization of the Results of the ML-Based GMDH Algorithm in order to Increase the Accuracy of Walnut Pollen Detection and Horizontal Optical Depth through the TLBO Algorithm

Document Type : Original Article

Authors

1 Prof. and Director of Information Technology, Dep. of Information Technology, Allameh Tabarsi Applied Science and Technology Center, University of Applied Science, Tehran, Iran

2 Assistant Prof. of Dep. of industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran

3 Ph.D. Student of Dep. of Health Information Technology, Faculty of Paramedical, Kermanshah University of Medical Sciences, Kermanshah, Tehran

4 Assistant Prof., Dep. of Computer Engineering, Faculty of Engineering, Arak University, Arak

Abstract

Introduction:  As one of the most essential needs of living beings, clean air quality has been threatened by natural and human activities. In recent years, dust storms have been increasing spatially and temporally, causing numerous damages to social, economic, and environmental health for the residents of the southern and southwestern regions of Iran. In the present study, MODIS sensor data were used to investigate dust storms and detect horizontal optical depth.
Materials and Methods: The  advantages of MODIS sensor data include high spectral and temporal resolution. Additionally, meteorological station data were collected based on the study period. After preprocessing the data and preparing field observations, the necessary features for modeling were extracted using the differential method between selected bands of each MODIS sensor image, along with features extracted from ground-based meteorological station sensors. After further investigations and evaluations and using the viewpoints of meteorological experts, 36 differential features from various MODIS image bands and six features from ground-based meteorological station data, totaling 42 features, were extracted. Subsequently, using feature selection techniques, the best features were identified. A novel method named ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning models, was employed to detect dust concentration and horizontal optical depth. To achieve optimal accuracy, the hyper-parameters of this model were heuristically tuned using the TLBO optimization algorithm. Additionally, machine learning methods such as Basic GMDH, SVM, MLP, MLR, RF, and their ensemble models were implemented to compare with the main approach. According to the results, the TLBO-tuned ML-Based GMDH method provided superior accuracy in detecting dust concentration compared to the aforementioned machine-learning methods.
Results and Discussion: The SVM-PSO method was selected as the best method in the feature selection phase, the RF method was chosen as the best method among basic classification methods, and the Ensemble SVM and Ensemble RF methods were selected as the best methods in the ensemble and classification phase. It was also observed that using the ensemble approach led to a desirable improvement in horizontal optical depth classification. In the second approach, a method titled ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning algorithms, was used for estimating dust concentration. Additionally, to achieve suitable accuracy, the hyper-parameters of this model were finely tuned using the TLBO optimization algorithm. The results showed that this method provided appropriate accuracy in estimating dust concentration and horizontal optical depth, out performing the best-selected methods from the first approach

Keywords

References

Amiri, M. & Soleimani, S.A., 2021, ML-Based Group Method of Data Handling: An Improvement on the Conventional GMDH, Complex Intell. Syst., 7, PP. 2949-2960.
Amiri, M. & Soleimani, S.A., 2022, Hybrid Atmospheric Satellite Image-Processing Method for Dust and Horizontal Visibility Detection through Feature Extraction and Machine Learning Techniques, J. Indian Soc. Remote Sens., 50, PP. 523-532.
Amiri, M., Soleimani, S.A. & Soltani Tafreshi, F., 2020, Dust and Sand Extraction from MODIS Satellite Imagery Using Artificial Neural Network, Iranian Journal of Remote Sensing and GIS, 12(1), PP. 37-54
Chen, Z., Chen, D., Xie, X., Cai, J., Zhuang, Y., Cheng, N., He, B. & Gao, B., 2019, Spatial Self-Aggregation Effects and National Division of City-Level PM2.5 Concentrations in China Based on Spatio-Temporal Clustering, Journal of Cleaner Production, 207, PP. 875-881.
Fan, W., Qin, K., Cui, Y., Li, D. & Bilal, M., 2020, Estimation of Hourly Ground-Level PM2.5 Concentration Based on Himawari-8 Apparent Reflectance, IEEE Transactions on Geoscience and Remote Sensing, 59(1), PP. 76-85.
Feng, L., Li, Y., Wang, Y. & Du, Q., 2020, Estimating Hourly and Continuous Ground-Level PM2.5 Concentrations Using an Ensemble Learning Algorithm: The ST-Stacking Model, Atmospheric Environment, 223, P. 117242.
Guo, J.-P., Zhang, X.-Y, CHE, H., Gong, S.-L., An, X., Cao, Ch., Guang, J., Zhang, H., Wang, Y., Zhang, X.-CH., Xue, M. & Li, X.-W., 2009, Correlation between PM Concentrations and Aerosol Optical Depth in Eastern China, Atmospheric Environment, 2009, 43(37), PP. 5876-5886.
Harba, H.S., Harba, E. & Farttoos, M., 2020, Prediction of Dust Storm Direction from Satellite Images by Utilized Deep Learning Neural Network, 2020 6th International Engineering Conference “Sustainable Technology and Development"(IEC), IEEE, Erbil, Iraq (23 June 2020), DOI.org/10.1038/s41612-023-00348-9.
Ji, L. & Fan, K., 2019, Climate Prediction of Dust Weather Frequency over Northern China Based on Sea-Ice Cover and Vegetation Variability, Climate Dynamics, 53(1-2), PP. 687-705.
Li, X. & Zhang, X., 2019, Predicting Ground-Level PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region: A Hybrid Remote Sensing and Machine Learning Approach, Environmental Pollution, 249, PP. 735-749.
Li, S., Zou, B., Fang, X. & Lin, Y., 2020, Time Series Modeling of PM2.5 Concentrations with Residual Variance Constraint in Eastern Mainland China during 2013-2017, Science of the Total Environment, 710, P. 135755.
Liu, G. & Park, S.U., 2007, The Logarithm-Linear Relationship of the Occurrence Frequency to the Duration of Sand-Dust Storms: Evidencee from Observationa Data in China, J. Arid. Environments, 71(2), PP. 243-249.
Park, S., Shin, M., Im, J., Song, C.-K., Choi, M., Kim, J., Lee, S., Park, R., Kim, J. & Lee, D.-W., 2019, Estimation of Ground-Level Particulate Matter Concentrations through the Synergistic Use of Satellite Observations and Process-Based Models over South Korea, Atmospheric Chemistry and Physics, 19(2), PP. 1097-1113.
Rahmati, O., Panahi, M., Ghiasi, S.S., Deo, R.C., Tiefenbacher, J.P., Pradhan, B., Jahani, A., Goshtasb, H., Kornejady, A. & Shahabi, H., 2020, Hybridized Neural Fuzzy Ensembles for Dust Source Modeling and Prediction, Atmospheric Environment, 224, P. 117320.
 Rezapour, K., Tavosi, T. & Khsrovi, M., 2010, A Study of the Causes of Formation of Arabian Dust Storms and Its Expansion over Iran, The Fourth International Congress of Geographers of the Islamic World, Zahedan.
Sahu, S.K., Sharma, S., Zhang, H., Chejarla, V., Guo, H., Hu, J., Ying, Q., Xing, J. & Kota, S.H., 2020, Estimating Ground Level PM2.5 Concentrations and Associated Health Risk in India Using Satellite Based AOD and WRF Predicted Meteorological Parameters, Chemosphere, 255, P. 126969.
Samadi, M. & Darvishi Boloorani, A., 2014, Global Dust Detection Index (GDDI); A New Remotely Sensed Methodology for Dust Storms Detection, Journal of Environmental Health Science & Engineering, 12, P. 20.
Shao, Y., Ma, Z., Wang, J. & Bi, J., 2020, Estimating Daily Ground-Level PM2.5 in China with Random-Forest-Based Spatiotemporal Kriging, Science of The Total Environment, 740, P. 139761.
Taghavi, F., Olad, A. & Safarrad, T., 2012, Highlighting the Dust Storms of Western Iran Using MODIS Spectral Features, Proceedings of the 15th Iran Geophysics Conference, Atmospheric Sciences Section, Ordibehesht, P. 109.
Wang, X. & Sun, W., 2019, Meteorological Parameters and Gaseous Pollutant Concentrations as Predictors of Daily Continuous PM2.5 Concentrations Using Deep Neural Network in Beijing–Tianjin–Hebei, China, Atmospheric Environment, 211, PP. 128-137.
Wang, W., Zhao, S., Jiao, L., Taylor, M., Zhang, B., Xu, G. & Hou, H., 2019, Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network, Scientific Reports, 9(1), PP. 1-10.
Yoon, J.H., Li, Y., Lee, M.S. & Jo, M., 2019, Deep Learning Drone Flying Height Prediction for Efficient Fine Dust Concentration Measurement, Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 (pp.1112-1119)a, International Conference on Ubiquitous Information Management and Communication, Springer.
Zamim, S.K., Faraj, N.S., Aidan, I.A., Al-Zwainy, F.M., AbdulQader, M.A. & Mohammed, I.A., 2019, Prediction of Dust Storms in Construction Projects Using Intelligent Artificial Neural Network Technology, Periodicals of Engineering and Natural Sciences, 7(4), PP. 1659-1666.
Zhao, T.X.P., 2012, Asian Dust Detection from the Satellite Observations of Moderate Resolution Imaging Spectroradiometer (MODIS), Aerosol and Air Quality Research, 12(6), PP. 1073-1080.
Zhao, T.X.P., Ackerman, S. & Guo, W., 2010, Dust and Smoke Detection for Multi Channel Imagers, International Journal of remote sensing, 2(10), PP. 2347-2367.

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