The Influence of Neural Networks on Hydropower Plant Management in
  Agriculture: Addressing Challenges and Exploring Untapped Opportunities
C. Coelho M. Fernanda P. Costa L. L. Ferrás
Hydropower plants are crucial for stable renewable energy and serve as vital
water sources for sustainable agriculture. However, it is essential to assess
the current water management practices associated with hydropower plant
management software. A key concern is the potential conflict between
electricity generation and agricultural water needs. Prioritising water for
electricity generation can reduce irrigation availability in agriculture during
crucial periods like droughts, impacting crop yields and regional food
security. Coordination between electricity and agricultural water allocation is
necessary to ensure optimal and environmentally sound practices. Neural
networks have become valuable tools for hydropower plant management, but their
black-box nature raises concerns about transparency in decision making.
Additionally, current approaches often do not take advantage of their potential
to create a system that effectively balances water allocation.
  This work is a call for attention and highlights the potential risks of
deploying neural network-based hydropower plant management software without
proper scrutiny and control. To address these concerns, we propose the adoption
of the Agriculture Conscious Hydropower Plant Management framework, aiming to
maximise electricity production while prioritising stable irrigation for
agriculture. We also advocate reevaluating government-imposed minimum water
guidelines for irrigation to ensure flexibility and effective water allocation.
Additionally, we suggest a set of regulatory measures to promote model
transparency and robustness, certifying software that makes conscious and
intelligent water allocation decisions, ultimately safeguarding agriculture
from undue strain during droughts.