The Forecasting of Groundwater Fluctuations is a useful tool for
managing groundwater resources in the mining area. Water resources
management requires identifying potential periods for groundwater
drainage to prevent groundwater from entering the mine pit and imposing
high costs. In this research, Auto-Regressive Integrated Moving Average
(ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models
were used for short-term modeling of the groundwater fluctuations in a
piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250
non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to
29-Jul-2019, 200 data for modeling, and 50 data for prediction were
used. To take advantage of all the features of the two developed models,
the predictions are combined with different methods and specific
weights. The results show better accuracy for the ARIMA method between
the two short-term forecasts, while the HWES method requires less time
for modeling. Also, among all the predictions made, the highest accuracy
for the combined least-squares method is for forecasting the groundwater
fluctuations in the short-term. All the forecasts show a decrease in the
groundwater fluctuations, indicating pumping wells around the Gohar
Zamin Iron Ore Mine area.
Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran