ABSTRACT
This chapter discusses the general overview of Artificial neural networks, and the different types, activation functions, learning paradigms, and application areas particularly relevant to hydrology and environmental systems. In certain situations, it may be desirable to use a linear activation function at the output layer. Possible structural mutations include node addition, node deletion, connection addition, connection deletion, and node fusion. The chapter presents a summary of the work reported elsewhere. The study to be presented here summarizes the work carried out in two coastal areas of Hong Kong: Tolo Harbour and Lamma Island. The processors operate on the data received via the connections. The transformation of an input to a corresponding output by a single neuron is relatively simple. The complexity arises as a result of the interactions of many neurons. The brain is a biological neural network with over 10 billion neurons and over 60 trillion synapses.
