Water Temperature Model: A Case Study in Alagoinhas-BA

Abstract

In recent years, climate change has generated disturbances at both local and global scales, directly impacting the water temperature of rivers. Temperature fluctuations have become a common occurrence in river water, where after experiencing heating, the return to its natural temperature is gradual. Consequently, temperature changes affect various internal processes within rivers, including nutrient consumption, food availability, and dissolved oxygen concentration. These alterations subsequently impact the growth and distribution of aquatic organisms. This article focuses on the physical variables derived from a model characterized by limited computational complexity. The final structure of the model consists of a single ordinary differential equation, which is linearly dependent on air and water temperature, as well as flow rate. Additionally, to handle data sequences such as time series more efficiently than traditional Recurrent Neural Networks (RNNs), we employ a Long Short-Term Memory (LSTM) neural network.