Empirical, Conventional to Neural Networks to Artificial Intelligence: Evolution of Water Resources Modelling in Rajasthan, India

Resumen

Water resources management in Rajasthan faces growing demands because of the unpredictable rainfall patterns, semi-arid weather conditions and the growing demand of water in the context of population growth and agricultural expansion. Therefore, proper evaluation of hydrological processes is important in watershed management and sustainable water planning in the area. Traditional hydrological modelling procedures have been used and applied extensively over the years to formulate prediction of rainfall runoff relationship, elements of water balance and rainfall runoff dynamics within the catchments of Rajasthan- the river basins. These techniques can be useful in studying the productivity of surface runoff, evapotranspiration losses and ground water recharge. But there are often complex and non-linear interactive processes between climatic variation, change in land use and basin characteristics, and there is often low predictive power in semi-arid that is found by simple techniques of modelling. Mathematical simulation with rainfall and runoff artificial neural networks under MATLAB showed a high predictive potential to semi arid catchment systems with rainfall that is not exceeding 0-180 mm/day, whereas runoff was reached to up to 95 m 3/s during turbulent seasons. The indices of the model showed that the performance of this model was consistent in the aspect of simulation accuracy in R2(0.72-90), radiation Nash Sutcliffe efficiency (0.66-0.89) and error analysis (RMSE = 4.5-12.8 M3/s; MAE =3.1-9.4 M3/s). The results imply that ANN-based hydrological modelling can give a solid foundation to assess rainfall-runoff relationships and improve watershed control models in semi-arid regions of Rajasthan. The ANN-based rainfall runoff model had excellent predictive quality regarding the semi arid basin with straw calculated R2 of 0.82 and NSE of 0.84 (almost comparable) between measured and simulated runoff. The indicators of errors were kept within normal standards (RMSE = 6.4 m 3/s; MAE = 4.9 m 3/s), low indicator of prediction bias (PBIAS = -3.8%). The results indicate that the neural network modelling paradigm would become a holistic analytical paradigm in estimation of runoff and watershed management in the semi arid basins of southeastern Rajasthan. Current computational approaches have become possible due to technological improvement, and this will allow the use of artificial intelligence applications in reference to artificial neural networking, machine learning and information-driven predictive models in water resources engineering. These methods would be able to model the nonlinear relationships between hydrological parameters and perform analysis of large hydro-meteorological data better. The AI-based models can possibly be used to enhance the prediction of rainfall, runoff, and drought in the Rajasthan region where there is wide variability in rainfall and hydrological unexpectedness. The correlation between the traditional hydrological models and the methodologies of artificial intelligence opens the way to the creation of the methodologies of more efficient evaluation of the water resources and hydrological prediction. Specifically, some of these hybrid modelling strategies such as those are able to result in superior watershed planning, more fitting management of reservoirs and decision-making on sustainable development procedure of water resources in Rajasthan.