Groundwater Level Forecasting Using Gravity Recovery and Climate Experiment (GRACE) By Artificial Neural Network (ANN) for Krishna River Sub-Basin.
Abstract
Groundwater will take part of a most important goal in satisfying the supply of water for different industry needs and requirements in our nation. The perpetually rising need for groundwater to cater the requirements of a large population of India has created a outcome in rigorous stress on the limited groundwater wealth available, heading to the steady turn down the level of ground water. In India, the natural wealth of the ground water has contested a crucial task in accomplishing the various needs of public house hold needs, various Developing manufacturing industry needs and agricultural sectors. In tremendous region of the world, the ground water management board resolutions are for the most part performed by in situational perception channels,, which, accidentally, have seen a rejection in coverage areas in current years. The REFINEMENT data, which provide information related to Terrestrial Water Storage change (TWAS) at regional as well as global scale, maybe use as an alternative for the same. In the current work, a (ANN) model is created to forecast groundwater level changes by utilizing month to month gridded GRACE information, Rainfall precipitation information, and the temperature information of the of the study area territory. The operating functions of the neural network in groundwater level forecasting is analyzed by five different criteria as “[Coefficient of Efficiency] CE, [Root Mean Square Error]- RMS), [Mea[Absolute Error ] -MAE, R2 Efficiency, and [Correlation Coefficient] CC “.
