EMPIRICAL CONVENTIONAL NEURAL NETWORKS ARTIFICIAL is a M.Tech project topic for Civil Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
EMPIRICAL CONVENTIONAL NEURAL NETWORKS ARTIFICIAL Project Details
| Abstract |
This project looks at how hydrological modeling is changing in semi‑arid areas, with a focus on the river basins of Rajasthan, India. In these regions, rainfall is hard to predict, evapotranspiration is high, and water demand keeps rising. Because of that, traditional empirical models often miss the strong non‑linear behavior of rainfall‑runoff processes. The study compares old‑style empirical methods with newer artificial neural networks (ANNs) and other AI tools. Using MATLAB‑based simulations, we test several neural‑network designs with different climate inputs and basin features. The model is run on historical daily rainfall records that vary from 0 to 180 mm per day, and it predicts peak runoff up to 95
m³/s. Results show that while conventional models give a basic estimate, ANN‑based models are much more accurate. They also capture the complex, non‑linear links between land‑use changes, climate variations, and how the catchment responds. The project offers step‑by‑step guidance for setting up, testing, and expanding machine‑learning models to help manage watersheds and plan water resources in arid and semi‑arid settings.
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| Reference Paper |
EMPIRICAL, CONVENTIONAL TO NEURAL NETWORKS TO ARTIFICIAL INTELLIGENCE: EVOLUTION OF WATER RESOURCES MODELLING IN RAJASTHAN, INDIA |
| Domain |
Civil Engineering |
| Sub-Domain |
Environmental & Water Resources / Hydrology & Hydraulics / Remote Sensing Hydrology |
| PDF Download |
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| Get Help |
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