Keywords: discrete wavelet transform, DWT, air quality, suspended particulate matter, SPM, ANFIS, adaptive neuro–fuzzy inference system, linear regression, air pollution, pollution modelling, coal mining, coal mines, opencast mining, neural networks, fuzzy logic, hybrid modelling, India, core zone, ambient temperature, wind speed, relative humidity
Wavelet–based models for air pollution modelling around coal mining sites in Jharkhand for 1, 3 and 5 day lead time
Coal dust is a major cause of air pollution in areas around opencast coal mining sites. Prediction of ambient concentration of pollutant should be known to implement any control techniques or to install any control equipment. Combining adaptive network based fuzzy inference system (ANFIS) and linear regression (LR) with discrete wavelet transform (DWT), two types of hybrid models were developed to forecast concentration of suspended particulate matter (SPM) for 1, 3 and 5–day–ahead around open cast coal mining sites in Jharkhand, India using DWT–obtained sub–time series of SPM and other relevant parameters, i.e., distance of point of forecast from core zone (x), ambient temperature (T), wind speed (w) and relative humidity (h). For the purpose of comparison ANFIS and LR models were also derived for the same lead time using original antecedent time series of SPM, T, w and h and distance of point of forecasting from the core zone. When compared, wavelet–based hybrid models predicted SPM with greater accuracy than ANFIS and LR models. Between the two types of hybrid models, the WANFIS type models gave slightly better results than the WR type.