• Adabala Soma Pradeep Verriyya
• Dr.V Anuragh
• Mr.A P V D L Kumar
• Mrs.Ch Santhi

Predicting groundwater levels with precision is critical for ensuring responsible and efficient water resource planning, especially in regions facing limited water availability. This research presents a hybrid approach that combines an Artificial Neural Network (ANN) with a Genetic Algorithm (GA) to enhance the accuracy of groundwater level forecasts. The ANN is utilized for its ability to learn and represent the nonlinear patterns present in hydrological data. Meanwhile, the GA is employed to fine-tune the network’s internal parameters, leading to improved predictive performance. The model is trained and tested using past groundwater level measurements alongside related climatic and geological data. Experimental results reveal that the proposed hybrid approach outperforms traditional ANN models by offering greater accuracy and robustness. This research provides a valuable predictive tool to assist decision-makers in managing groundwater resources more efficiently.

Artificial Neural Network , Genetic Algorithm, Hybrid Model ,Groundwater Management, Crow Search Algorithm ,Gray Wolf Algorithm

"PREDICTION OF GROUND WATER LEVELS USING GENETIC ALGORITHM BASED HYBRID ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.h894-h898, May-2025, Available :http://www.jetir.org/papers/JETIR2505892.pdf

"PREDICTION OF GROUND WATER LEVELS USING GENETIC ALGORITHM BASED HYBRID ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pph894-h898, May-2025, Available at : http://www.jetir.org/papers/JETIR2505892.pdf