Advances in Engineering Software
Theoretical Computer Science
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial neural network based predictive method for flood disaster
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Genetic Algorithm Implementation in Python
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Environmental Modelling & Software
Time series prediction with single multiplicative neuron model
Applied Soft Computing
Crowding clustering genetic algorithm for multimodal function optimization
Applied Soft Computing
Representation of functional data in neural networks
Neurocomputing
An innovative risk evaluation system estimating its own fuzzy entropy
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
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The present manuscript is the result of research conducted towards a wider use of artificial neural networks in the management of mountainous water supplies. The novelty lies on the evolutionary clustering of time-series data which are then used for the training and testing of a neural object, applying meta-heuristics in the neural training phase, for the management of water resources and for torrential risk estimation and modelling. It is essentially an attempt towards the development of a more credible forecasting system, exploiting an evolutionary approach used to interpret and model the significance which time-series data pose on the behavior of the aforementioned environmental reserves. The proposed model, designed such as to effectively estimate the average annual water supply for the various mountainous watersheds, accepts as inputs a wide range of meta-data produced via an evolutionary genetic process. The data used for the training and testing of the system refer to certain watersheds spread over the island of Cyprus and span a wide temporal period. The method proposed incorporates an evolutionary process to manipulate the time-series data of the average monthly rainfall recorded by the measuring stations, while the algorithm includes special encoding, initialization, performance evaluation, genetic operations and pattern matching tools for the evolution of the time-series into significantly sampled data.