Hydrological Data Driven Modelling: A Case Study Approach by Renji Remesan, Jimson Mathew

By Renji Remesan, Jimson Mathew

This e-book explores a brand new realm in data-based modeling with purposes to hydrology. Pursuing a case research procedure, it provides a rigorous evaluate of cutting-edge enter choice tools at the foundation of certain and accomplished experimentation and comparative experiences that hire rising hybrid suggestions for modeling and research. complex computing deals a variety of new recommendations for hydrologic modeling with assistance from mathematical and data-based methods like wavelets, neural networks, fuzzy common sense, and help vector machines. lately laptop learning/artificial intelligence concepts have emerge as used for time sequence modeling. even if, although preliminary reports have proven this method of be potent, there are nonetheless matters approximately their accuracy and skill to make predictions on a specific enter space.

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Some companies working on commercial neural network software development adopt a rule of thumb of the sum of input and output nodes multiplied by 2/3 as the indicator to choose the number of hidden neurons. Swingler [73] suggests that, for networks with one hidden layer, the model give better performance if we use twice the number of input nodes in the hidden layer. At the same time, Berry and Linoff [13] note that the number of hidden nodes should never be more than double the nodes in the input layer.

In this approach research normally depends on linear cross-correlation analysis values to determine the strength of the relationship between the input time series and the output time series at various lags [31]. The disadvantage associated with this method is its inability to capture any nonlinear dependence that may exist between the inputs and the output. The cross-correlation method works on linear dependence between two variables, so there is a good chance of the omission of important inputs that are related to the output in a nonlinear fashion.

Some companies working on commercial neural network software development adopt a rule of thumb of the sum of input and output nodes multiplied by 2/3 as the indicator to choose the number of hidden neurons. Swingler [73] suggests that, for networks with one hidden layer, the model give better performance if we use twice the number of input nodes in the hidden layer. At the same time, Berry and Linoff [13] note that the number of hidden nodes should never be more than double the nodes in the input layer.

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