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Title

OFFERING ACCURATE METHOD FOR FORECASTING GROUNDWATER LEVEL

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Abstract

GROUNDWATER LEVEL IS MAIN FACTOR FOR PLANNING INTEGRATED MANAGEMENT OF GROUNDWATER AND SURFACE WATER COURSES IN A BASIN. THEREFORE, DETERMINE A ROBUST METHOD IS IMPORTANT TO ASSESS THE SPATIALLY VARIABILITY OF GROUNDWATER LEVEL AT DIFFERENT METHODS. IN THIS STUDY, GROUNDWATER LEVEL WAS INTERPOLATED BY ARTIFICIAL NEURAL NETWORK (ANN) AND GEOSTATISTICAL METHODS. THE DATA SET CONSISTS OF MONTHLY GROUNDWATER LEVELS MEASURED AT 168 OBSERVATION WELLS FROM 2008 TO 2014 IN AQUIFER QAZVIN, IRAN. DIFFERENT TYPES OF NETWORK ARCHITECTURES AND TRAINING ALGORITHMS ARE INVESTIGATED AND COMPARED IN TERMS OF MODEL PREDICTION EFFICIENCY AND ACCURACY. ALSO KRIGING METHOD WITH DIFFERENT MODELS IS INVESTIGATED. FINALLY BEST METHODS OBTAIN BY CALCULATING ROOT MEAN SQUARE ERROR, CORRELATION COEFFICIENT CORRELATION.

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