In the heart of India’s Mahanadi Basin, where agriculture and water management hinge on precise predictions, a new study by Deepak Raj of the National Institute of Technology Patna offers a glimpse into the future of evapotranspiration (ETo) forecasting—a critical factor for irrigation, energy production, and climate resilience. The research, published in *Ingeniería e Investigación* (Engineering and Research), pits three machine learning models against each other to determine which best predicts actual ETo across six key stations: Raipur, Korba, Jharsuguda, Bilaspur, Bhubaneswar, and Balangir.
The stakes are high. Evapotranspiration—the combined process of water evaporation from soil and transpiration from plants—drives water demand in agriculture, influences hydropower generation, and shapes drought forecasting. Yet, traditional methods often fall short in capturing the complexity of local climate and terrain. Enter artificial intelligence. Raj and his team evaluated M5P (a model tree algorithm), ANFIS (adaptive neuro-fuzzy inference system), and GEP (gene expression programming) using rigorous metrics like R², RMSE, NSE, and MAE.
The results were decisive. ANFIS emerged as the standout performer, delivering consistently high accuracy across most stations. “ANFIS didn’t just meet expectations—it redefined them,” Raj noted. “Its ability to adapt to varying conditions while maintaining low error margins makes it a game-changer for water resource planning.” With R² values nearing 0.99 and RMSE as low as 0.10, ANFIS demonstrated a rare balance of precision and reliability.
Notably, the model’s dominance wasn’t absolute. In Bhubaneswar, GEP outperformed ANFIS, while M5P took the lead in Balangir. This variability underscores a key insight: no single model fits all scenarios. “The takeaway isn’t that ANFIS is universally superior,” Raj explained. “It’s that the right tool must align with the local environment. Tailoring models to specific regions could unlock even greater efficiencies.”
For the energy sector, the implications are profound. Hydropower plants, thermal cooling systems, and even solar farms rely on accurate water availability forecasts. ANFIS’s ability to refine ETo predictions could lead to better reservoir management, reduced water waste in cooling processes, and more stable energy output—especially in drought-prone regions. Meanwhile, agricultural planners could use these insights to optimize irrigation schedules, cutting costs and conserving water.
The study also hints at a broader trend: the rise of hybrid AI approaches. By combining neural networks, fuzzy logic, and evolutionary algorithms, researchers are pushing the boundaries of what’s possible in environmental modeling. As climate patterns grow more erratic, tools like ANFIS may become indispensable for balancing water demand with energy production.
Published in *Ingeniería e Investigación*, this work joins a growing body of research validating AI’s role in water science. Yet, as Raj cautions, the journey is just beginning. “We’re only scratching the surface,” he said. “The next step is integrating more variables—soil moisture, wind patterns, even socioeconomic factors—to build even smarter systems.”
For policymakers, engineers, and energy executives, the message is clear: the future of water and power may well be written in code. And in the Mahanadi Basin, at least, ANFIS is leading the way.