A Broad Literature Survey of Development and Application of Artificial Neural Networks in Rainfall-Runoff Modelling

Deep learning, a subset of AI, has gained popularity in various fields, including computer vision and NLP. It is based on artificial neural networks, which process multiple layers of data and extract high-level features automatically. Unlike traditional ML algorithms, deep learning can process large unstructured data and complex algorithms better than traditional methods. The human brain inspires neural networks, which contain artificial neurons similar to biological neurons. These networks are made up of three layers: input, hidden, and output. Deep learning maps inputs to outputs and finds correlations, making it a “universal approximator.” It can be combined with other AI methods to perform more complex tasks, such as deep reinforcement learning. Top companies using ANN include Nvidia Corp., Alphabet, Salesforce.com, Amazon.com, Microsoft Corp., Twilio, IBM, and Facebook. Deep learning uses ANN to analyze data and make predictions and has found applications in almost every business sector.

show abstract

A Broad Literature Survey of Development and Application of Artificial Neural Networks in Rainfall-Runoff Modelling

Cited by 2 publications

References 53 publications

Performance of optimum neural network in rainfall–runoff modeling over a river basin

Performance of optimum neural network in rainfall–runoff modeling over a river basin

Neural Network and Neural Computing

Contact Info

Product

Resources

About