Do Internals of Neural Networks Make Sense in the Context of Hydrology?

Mani Manavalan

doi:10.18034/ajase.v7i1.48

Authors

Mani Manavalan Technical Project Manager, Larsen & Toubro Infotech (LTI), Mumbai, INDIA

Keywords:

Neural Networks

Hydrology

Hydroinformatics system

Abstract

As of recent times, neural networks have drawn in a lot of attention and popularity because of their application to numerous dimensions, including computer visioning and the processing of natural language. Nevertheless, when it comes to the science applicable to the immediate environment, such as the run-off of rainfall modeling in the field of hydrology, and so being, neural networks have the tendency to have a surprisingly demeaning reputation. This bad record can be blamed on the reality that they are black-boxed in primal nature, as well as to the complication or impossibility to comprehend internals that culminates in a forecast. Neural systems make up a computational technology tailored for hydrological predictions. In spite of being extensively utilized in many other fields of research and application, the number of hydrologists that favor the methodology may be lower than expected. That’s as a result of the neural network’s data-driven nature for the applied challenges looking to be solved. Also, neural systems make provision for a modeling footprint that comes in handy when there is a sufficiency of raw information to connect X with Y, particularly in the cases where there is a need for real-time results. With this paper, we are introducing neural ecosystem challenges in generality, setting them in more extensive modeling in a relational context with hydrology. This study explores the internals of the learned networks for different basin sets from the sundrily obtainable dataset known as CAMELS. Also, it utilizes local sensing data in the symbolism of patterns in alignment with our comprehension of the system of hydrology. Demonstratively, we showcase that internally, LSTMs learned to interpret patterns that align with our comprehension of the hydrological system. For snow-carried catchments, for instance, the network grows specific memory cells with the capacity to imitate conceptual snow memories with yearly dynamics. This is known and accepted for the catchment models based on processes.

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