Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

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.

[1]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[2]  Mohd Azlan Hussain,et al.  Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network , 2002 .

[3]  Lothar M. Schmitt,et al.  Theory of genetic algorithms , 2001, Theor. Comput. Sci..

[4]  Qing Ling,et al.  Crowding clustering genetic algorithm for multimodal function optimization , 2008, Appl. Soft Comput..

[5]  N. S. Visen,et al.  AE—Automation and Emerging Technologies: Evaluation of Neural Network Architectures for Cereal Grain Classification using Morphological Features , 2001 .

[6]  Lazaros S. Iliadis,et al.  An innovative risk evaluation system estimating its own fuzzy entropy , 2007, Math. Comput. Model..

[7]  Hiok Chai Quek,et al.  RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction , 2007, Neurocomputing.

[8]  Yi-Ming Wei,et al.  Artificial neural network based predictive method for flood disaster , 2002 .

[9]  Christian W. Dawson,et al.  The effect of different basis functions on a radial basis function network for time series prediction: A comparative study , 2006, Neurocomputing.

[10]  Tienfuan Kerh,et al.  Neural networks forecasting of flood discharge at an unmeasured station using river upstream information , 2006, Adv. Eng. Softw..

[11]  Mohammad Karamouz,et al.  A stochastic conflict resolution model for water quality management in reservoir–river systems , 2007 .

[12]  R. K. Agrawal,et al.  Application of a Genetic Algorithm in the Development and Optimisation of a Non-linear Dynamic Runoff Model , 2003 .

[13]  Lionel Boillereaux,et al.  Thermal properties estimation during thawing via real-time neural network learning , 2003 .

[14]  Chuntian Cheng,et al.  Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration , 2002 .

[15]  J. R. Ni,et al.  Application of artificial neural network to the rapid feedback of potential ecological risk in flood diversion zone , 2003 .

[16]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..

[17]  James B. McDonald,et al.  Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models , 1999, Comput. Intell..

[18]  François Anctil,et al.  Improvement of rainfall-runoff forecasts through mean areal rainfall optimization , 2006 .

[19]  George J. M. Aitken,et al.  Genetic algorithm design of complexity-controlled time-series predictors , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[20]  Éric D. Taillard,et al.  Analysis and test of efficient methods for building recursive deterministic perceptron neural networks , 2007, Neural Networks.

[21]  Mikko Kolehmainen,et al.  Evolving the neural network model for forecasting air pollution time series , 2004, Eng. Appl. Artif. Intell..

[22]  Inmaculada Pulido-Calvo,et al.  Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds , 2007 .

[23]  Shyam S. Sablani,et al.  Neural networks for predicting thermal conductivity of bakery products , 2002 .

[24]  Lothar M. Schmitt,et al.  Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling , 2004, Theor. Comput. Sci..

[25]  Christian Igel,et al.  Improving the Rprop Learning Algorithm , 2000 .

[27]  Ali Yalcin,et al.  Flood prediction using Time Series Data Mining , 2007 .

[28]  L. Bodri,et al.  Prediction of extreme precipitation using a neural network: application to summer flood occurence in Moravia , 2000 .

[29]  S IliadisLazaros,et al.  An Artificial Neural Network model for mountainous water-resources management , 2007 .

[30]  S. Ashforth-Frost,et al.  Evaluating convective heat transfer coefficients using neural networks , 1996 .

[31]  Prem Kumar Kalra,et al.  Time series prediction with single multiplicative neuron model , 2007, Appl. Soft Comput..

[32]  Kwok-wing Chau A split-step particle swarm optimization algorithm in river stage forecasting , 2007 .

[33]  L. Schmitt Fundamental Study Theory of genetic algorithms , 2001 .

[34]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[35]  Samir W. Mahfoud Crossover interactions among niches , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[36]  Chuntian Cheng,et al.  Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure , 2006 .

[37]  Mark Watson Neural Network Library , 1996 .

[38]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[39]  Michel Verleysen,et al.  Representation of functional data in neural networks , 2005, Neurocomputing.

[40]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[41]  Teresa Bernarda Ludermir,et al.  Meta-learning approaches to selecting time series models , 2004, Neurocomputing.

[42]  Wonjae Lee,et al.  Genetic algorithm implementation in Python , 2005, Fourth Annual ACIS International Conference on Computer and Information Science (ICIS'05).

[43]  Cláudia Cristina dos Santos,et al.  Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data , 2006 .

[44]  Lazaros S. Iliadis,et al.  An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds , 2007, Environ. Model. Softw..

[45]  Sreeram Ramakrishnan,et al.  A hybrid approach for feature subset selection using neural networks and ant colony optimization , 2007, Expert Syst. Appl..

[46]  Hosahalli S. Ramaswamy,et al.  PREDICTION OF PSYCHROMETRIC PARAMETERS USING NEURAL NETWORKS , 1998 .

[47]  Leon S. Lasdon,et al.  Solving nonlinear water management models using a combined genetic algorithm and linear programming approach , 2001 .

[48]  Lazaros S. Iliadis,et al.  Fundamental fuzzy relation concepts of a D.S.S. for the estimation of natural disasters' risk (The case of a trapezoidal membership function) , 2005, Math. Comput. Model..

[49]  C. L. Changa,et al.  Applying fuzzy theory and genetic algorithm to interpolate precipitation , 2005 .

[50]  Shyam S. Sablani,et al.  UNIFICATION OF FRUIT WATER SORPTION ISOTHERMS USING ARTIFICIAL NEURAL NETWORKS , 2001 .

[51]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[52]  Peter C Young,et al.  Advances in real–time flood forecasting , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[53]  G. Sahoo,et al.  Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii , 2006 .