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Vol. 10, Issue 4 (2021)

Forecasting of water table fluctuation for Priyadarshini watershed using Ann with different algorithms

Author(s):
PB Hittanagi, HN Bhange, BL Ayare and MS Mane
Abstract:
Groundwater is an important natural resource essential for sustenance of life. Over 98% of the freshwater on the Earth lies below its surface. The ANN technique is applied as a new approach and an attractive tool to study and predict groundwater levels without applying physically based hydrologic parameters. It is observed that the maximum LM value of R for training and validation are 0.908 and 0.903 shown in well 2 (2-9-1) and testing is 0.949 shown in well 8 (2-9-1), whereas minimum value for training is 0.684 shown in well 1 (4-4-1) for validation and testing are 0.159 and 0.773 well 9 (3-5-1) The maximum observed R value for CG training, validation and testing are 0.76 shown in well 3 (4-5-1), 0.85 shown in well 9(3-5-1) and 0.891 shown in well 7 (2-5-1) whereas the minimum value for training and testing are 0.671 and 0.458 shown in well 1 (4-4-1) for validation the minimum R value is 0.638 well 2 (4-7-1). Considering training, validation and testing period and all the statistics it is difficult to say which algorithm is better among the two selected for study. Because there was a lot of variation in all the statistics among the two selected algorithms for training, validation and testing period. But considering the testing period of all the nine wells it was found that LM algorithm was better than CG for wells i.e., well 1 (2-9-1), well 2 (2-9-1), well 3 (1-8-1), well 4 (1-6-1), well 5 (2-9-1), well 6 (1-9-1), well 8 (2-9-1) while CG algorithm was better than LM for wells i.e., well 7 (2-5-1) and well 9 (3-5-1) So these algorithms for particular well were selected for sensitivity analysis.
Pages: 386-391  |  407 Views  90 Downloads


The Pharma Innovation Journal
How to cite this article:
PB Hittanagi, HN Bhange, BL Ayare, MS Mane. Forecasting of water table fluctuation for Priyadarshini watershed using Ann with different algorithms. Pharma Innovation 2021;10(4):386-391. DOI: 10.22271/tpi.2021.v10.i4f.5962
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