Although the concept of water security is relatively new, but such parameters can prevent wastage of water which is important for the impending days of water scarcity.Water security defines the availability of a certain amount of water which will be accessible and utilizable even under massive scales of uncertainity in the climate and total failure of conservation efforts.The water security of any region is directly proportional to the minimum water available to sustain the demand.
Most hydrological variables are strongly time‑dependent. River discharge, groundwater level, rainfall, evapotranspiration, even reservoir storage – all of these change over hours, days, seasons, and years. Capturing this temporal behaviour accurately is at the heart of modern water resources modelling and management. Artificial Neural Networks (ANNs) have emerged as a powerful tool for dealing with such time‑dependent problems. They are data‑driven, flexible, and capable of learning complex nonlinear relationships that are often difficult to express analytically. Even though the exact theoretical relationship between time dependence in hydrological variables and neural network architecture is still an open research question, experience shows that ANNs can deliver more accurate predictions than many traditional hydrological models in a wide range of applications. This post outlines a practical methodology for applying neural networks to develop hydrological prediction models, especially...
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