Optimizing the results of ML-Based GMDH algorithm in order to increase the accuracy of dust detection and horizontal visibility depth through TLBO algorithm

Document Type : Original Article


1 Professor and Director of Information Technology Department, Allameh Tabarsi Center for Applied Science and Technology - Official Officer of the Ministry of Economic Affairs and Finance

2 Faculty of Industry, Kermanshah University of Technology

3 Employee of Paramedical School of Kermanshah University of Medical Sciences

4 Assistant Professor, Department of Computer Engineering, Arak University


Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern and southwestern regions of Iran. In this study, (MODIS) sensor data are used to investigate dust storms and detect horizontal depth of field. The advantages of MODIS sensor data include high spectral and temporal resolution. In addition, data from meteorological stations are collected according to the study period. After pre-processing data and preparing field observations, features required for modeling are derived from the MODIS sensor data through a differential method between the selected bands of each MODIS sensor image along with the features extracted from the sensor. Ground meteorological stations are used. With further studies and evaluations and using the opinions of meteorological experts 42 features are used of which36 are extracted from different bands of Moody's images and 6 features are extracted from meteorological station data. Next, best features are identified through feature selection techniques and a new method called ML-Based GMDH. which is the result of improving the GMDH neural network by changing partial functions with machine learning models, was used to detect dust concentration and horizontal visibility. In addition, to achieve the appropriate accuracy, the meta-parameters of this model are adjusted by the TLBO optimization algorithm. Furthermore, basic GMDH machine learning methods SVM, MLP, MLR, RF and their group model are implemented to compare with the main approach. Results shows that the ML-Based GMDH method adjusted with TLBO by improving on the best methods. The machine learner provides good accuracy in detecting dust concentrations.