Application of Artificial Neural Networks to the modeling of rain-runoff in the Chancay Lambayeque river basin

Abstract

Between the months of December to April, regions of northern Peru, including Lambayeque, are affected by maximum extreme events, wreaking havoc on homes, flooding crop fields, collapsing hydraulic works, and the most irreparable loss of human lives. In this line, the objective of this research was to apply Artificial Neural Networks to rain-runoff modeling in a basin in northern Peru, namely, the Chancay Lambayeque river basin belonging to the Pacific slope. For this purpose, records of precipitation and flows of 30 years (hydrological normal) were collected from 12 hydrometeorological stations belonging to the basin and neighboring it. Thus, applying a model of Long and Short Term Memory Networks (LSTM) we proceeded to model the rain, seeking to follow the behavior of the flows observed in the Racarrumi hydrometric station, with 80% of the information the model was trained and with 20% it was validated. In short, it was obtained that in the modeling validation stage, the Nash coefficient was 0.93, corresponding to the qualifier "very good".