Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation

One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation that allows the lateral displacement of a label considering an unique parameter. This way to work involves a reduction of the search space that eases the derivation of optimal models and therefore, improves the mentioned trade-off. Based on the 2-tuples rule representation, this work proposes a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (number of labels and lateral displacements) and a simple rule generation method to quickly learn the associated rule base. Since this rule generation method is run from each data base definition generated by the evolutionary algorithm, its selection is an important aspect. In this work, we also propose two new ad hoc data-driven rule generation methods, analyzing the influence of them and other rule generation methods in the proposed learning approach. The developed algorithms will be tested considering two different real-world problems.Spanish Ministry of Science and Technology under Projects TIC-2002-04036-C05-01 and TIN-2005-08386-C05-0

Urban regeneration through retroftting social housing: the AURA 3.1 prototype

As a large proportion of housing stock does not meet the current demands for energy and comfort (leading to high levels of obsolescence and vulnerability), the annual rate of energy upgrades for the existing stock must be increased. The AURA Strategy is an intervention methodology which focuses on the regeneration of neighbourhoods or obsolete urban fabrics which sufer from high levels of architectural, urban and socioeconomic vulnerability. Within this context, the AURA 3.1 prototype was developed for the Solar Decathlon Europe 2019 Competition. The project was based around a sustainable construction strategy for the urban regeneration of obsolete residential neighbourhoods, through the reuse of existing buildings considering Mediterranean climate and energy. The Poligono San Pablo neighbourhood was chosen as the case study. This article presents the main retroft action: the juxtaposition on the existing building of a structural-architectural system which provides new technological and spatial features. Quantitative data regarding the validity and efectiveness of the AURA Strategy could be collected from the monitoring of the Pavilion prototype during the competition. Two frst prizes were won in contests with on-site measurements: Comfort conditions and House Functioning. Third place was also obtained in the Sustainability contest, thus confrming the enormous possibilities the AURA Strategy has for sustainable urban regeneration in retroftting social housing, within the limitations of the competition

Student competitions as a learning method with a sustainable focus in higher education: the University of Seville “Aura Projects” in the “Solar Decathlon 2019”

In recent times, teaching in higher education has undergone a significant transformation. Current advances and innovative proposals in educational science research are centred around a transdisciplinary approach, the so-called integrated curriculum and the incorporation of the transversal concept of sustainability. In summary, the so-called learning processes through problem-solving. The Solar Decathlon Competition is the most prestigious international university student competition for sustainable habitat. The aim of this article is to show how the Aura Strategy, developed by the University of Seville Solar Deca thlon Team to participate in the Solar Decathlon 2019 Latin America and Europe competitions, is aligned with the aforementioned proposals. Among the results, the generation of a transforming teaching network of the departmental structures in the University of Seville is to be highlighted. These transformations in teaching lead students to new, broader and more holistic approaches to study, as well as new capabilities and skills. The question of interdisciplinarity requires new tools and research lines to achieve successful implementation in higher education, and the participation in the Solar Decathlon Competition is one of the

Water Distribution System Computer-Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDS), including the sizing of components, quality control, reliability, renewal and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly-dimensional, multimodal, non-linear problems, especially given inaccurate, noisy, discrete and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent based systems. It is aimed at supporting decisionmaking processes by solving multi-objective optimization problems. ASO offers robustness through a framework where various population-based algorithms co-exist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Spanish Ministry of Education and Science, and ACOMP/2010/146 of the education department of the Generalitat Valenciana. The use of English was revised by John Rawlins.Montalvo Arango, I.; Izquierdo Sebastián, J.; Pérez García, R.; Herrera Fernández, AM. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering. 29(6):433-448. https://doi.org/10.1111/mice.12062433448296Adeli, H., & Kumar, S. (1995). Distributed Genetic Algorithm for Structural Optimization. Journal of Aerospace Engineering, 8(3), 156-163. doi:10.1061/(asce)0893-1321(1995)8:3(156)Afshar, M. H., Akbari, M., & Mariño, M. A. (2005). Simultaneous Layout and Size Optimization of Water Distribution Networks: Engineering Approach. Journal of Infrastructure Systems, 11(4), 221-230. doi:10.1061/(asce)1076-0342(2005)11:4(221)Amini, F., Hazaveh, N. K., & Rad, A. A. (2013). Wavelet PSO-Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers. Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-557. doi:10.1111/mice.12017Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Badawy, R., Yassine, A., Heßler, A., Hirsch, B., & Albayrak, S. (2013). A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid. Integrated Computer-Aided Engineering, 20(2), 127-141. doi:10.3233/ica-130423Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. doi:10.1007/bf00940812Dandy, G. C., & Engelhardt, M. O. (2006). Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains. Journal of Water Resources Planning and Management, 132(2), 79-88. doi:10.1061/(asce)0733-9496(2006)132:2(79)Díaz , J. L. Herrera , M. Izquierdo , J. Montalvo , I. Pérez-García , R. 2008 A particle swarm optimization derivative applied to cluster analysisDorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436Dridi, L., Parizeau, M., Mailhot, A., & Villeneuve, J.-P. (2008). Using Evolutionary Optimization Techniques for Scheduling Water Pipe Renewal Considering a Short Planning Horizon. Computer-Aided Civil and Infrastructure Engineering, 23(8), 625-635. doi:10.1111/j.1467-8667.2008.00564.xDuan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3), 501-521. doi:10.1007/bf00939380Duchesne, S., Beardsell, G., Villeneuve, J.-P., Toumbou, B., & Bouchard, K. (2012). A Survival Analysis Model for Sewer Pipe Structural Deterioration. Computer-Aided Civil and Infrastructure Engineering, 28(2), 146-160. doi:10.1111/j.1467-8667.2012.00773.xDupont, G., Adam, S., Lecourtier, Y., & Grilheres, B. (2008). Multi objective particle swarm optimization using enhanced dominance and guide selection. International Journal of Computational Intelligence Research, 4(2). doi:10.5019/j.ijcir.2008.134Fougères, A.-J., & Ostrosi, E. (2013). Fuzzy agent-based approach for consensual design synthesis in product configuration. Integrated Computer-Aided Engineering, 20(3), 259-274. doi:10.3233/ica-130434Fuggini, C., Chatzi, E., & Zangani, D. (2012). Combining Genetic Algorithms with a Meso-Scale Approach for System Identification of a Smart Polymeric Textile. Computer-Aided Civil and Infrastructure Engineering, 28(3), 227-245. doi:10.1111/j.1467-8667.2012.00789.xZong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)Goulter, I. C., & Bouchart, F. (1990). Reliability‐Constrained Pipe Network Model. Journal of Hydraulic Engineering, 116(2), 211-229. doi:10.1061/(asce)0733-9429(1990)116:2(211)Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. Journal of Transportation Engineering, 112(3), 287-301. doi:10.1061/(asce)0733-947x(1986)112:3(287)Gupta, R., & Bhave, P. R. (1994). Reliability Analysis of Water‐Distribution Systems. Journal of Environmental Engineering, 120(2), 447-461. doi:10.1061/(asce)0733-9372(1994)120:2(447)Gutierrez-Garcia, J. O., & Sim, K. M. (2012). Agent-based cloud workflow execution. Integrated Computer-Aided Engineering, 19(1), 39-56. doi:10.3233/ica-2012-0387Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P., & Yu, W.-D. (2012). Neuro-Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction. Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-781. doi:10.1111/j.1467-8667.2012.00786.xIzquierdo , J. Minciardi , R. Montalvo , I. Robba , M. Tavera , M. 2008a Particle swarm optimization for the biomass supply chain strategic planning 1272 80Izquierdo , J. Montalvo , I. Herrera , M. Pérez-García , R. 2012 A general purpose non-linear optimization framework based on particle swarm optimizationIzquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Izquierdo , J. Montalvo , I. Pérez , R. Tavera , M. 2008b Optimization in water systems: a PSO approach 239 46Jafarkhani, R., & Masri, S. F. (2010). Finite Element Model Updating Using Evolutionary Strategy for Damage Detection. Computer-Aided Civil and Infrastructure Engineering, 26(3), 207-224. doi:10.1111/j.1467-8667.2010.00687.xJanson, S., Merkle, D., & Middendorf, M. (2008). Molecular docking with multi-objective Particle Swarm Optimization. Applied Soft Computing, 8(1), 666-675. doi:10.1016/j.asoc.2007.05.005Kalungi, P., & Tanyimboh, T. T. (2003). Redundancy model for water distribution systems. Reliability Engineering & System Safety, 82(3), 275-286. doi:10.1016/s0951-8320(03)00168-6Keedwell, E., & Khu, S.-T. (2006). Novel Cellular Automata Approach to Optimal Water Distribution Network Design. Journal of Computing in Civil Engineering, 20(1), 49-56. doi:10.1061/(asce)0887-3801(2006)20:1(49)Kennedy , J. Eberhart , R. C. 1995 Particle swarm optimization 1942 48Khomsi, D., Walters, G. A., Thorley, A. R. D., & Ouazar, D. (1996). Reliability Tester for Water-Distribution Networks. Journal of Computing in Civil Engineering, 10(1), 10-19. doi:10.1061/(asce)0887-3801(1996)10:1(10)KIM, H., & ADELI, H. (2001). DISCRETE COST OPTIMIZATION OF COMPOSITE FLOORS USING A FLOATING-POINT GENETIC ALGORITHM. Engineering Optimization, 33(4), 485-501. doi:10.1080/03052150108940930Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Kleiner, Y., Adams, B. J., & Rogers, J. S. (2001). Water Distribution Network Renewal Planning. Journal of Computing in Civil Engineering, 15(1), 15-26. doi:10.1061/(asce)0887-3801(2001)15:1(15)Martínez-Rodríguez, J. B., Montalvo, I., Izquierdo, J., & Pérez-García, R. (2011). Reliability and Tolerance Comparison in Water Supply Networks. Water Resources Management, 25(5), 1437-1448. doi:10.1007/s11269-010-9753-2Montalvo Arango, I. (s. f.). Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization. doi:10.4995/thesis/10251/14858Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Montalvo, I., Izquierdo, J., Pérez, R., & Iglesias, P. L. (2008). A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Engineering Optimization, 40(7), 655-668. doi:10.1080/03052150802010607Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization applied to the design of water supply systems. Computers & Mathematics with Applications, 56(3), 769-776. doi:10.1016/j.camwa.2008.02.006Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Moscato , P. 1989 On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic AlgorithmsNejat, A., & Damnjanovic, I. (2012). Agent-Based Modeling of Behavioral Housing Recovery Following Disasters. Computer-Aided Civil and Infrastructure Engineering, 27(10), 748-763. doi:10.1111/j.1467-8667.2012.00787.xPark, H., & Liebman, J. C. (1993). Redundancy‐Constrained Minimum‐Cost Design of Water‐Distribution Nets. Journal of Water Resources Planning and Management, 119(1), 83-98. doi:10.1061/(asce)0733-9496(1993)119:1(83)Paya, I., Yepes, V., González-Vidosa, F., & Hospitaler, A. (2008). Multiobjective Optimization of Concrete Frames by Simulated Annealing. Computer-Aided Civil and Infrastructure Engineering, 23(8), 596-610. doi:10.1111/j.1467-8667.2008.00561.xPinto, T., Praça, I., Vale, Z., Morais, H., & Sousa, T. M. (2013). Strategic bidding in electricity markets: An agent-based simulator with game theory for scenario analysis. Integrated Computer-Aided Engineering, 20(4), 335-346. doi:10.3233/ica-130438Putha, R., Quadrifoglio, L., & Zechman, E. (2011). Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions. Computer-Aided Civil and Infrastructure Engineering, 27(1), 14-28. doi:10.1111/j.1467-8667.2010.00715.xRaich, A. M., & Liszkai, T. R. (2011). Multi-objective Optimization of Sensor and Excitation Layouts for Frequency Response Function-Based Structural Damage Identification. Computer-Aided Civil and Infrastructure Engineering, 27(2), 95-117. doi:10.1111/j.1467-8667.2011.00726.xRodríguez-Seda, E. J., Stipanović, D. M., & Spong, M. W. (2012). Teleoperation of multi-agent systems with nonuniform control input delays. Integrated Computer-Aided Engineering, 19(2), 125-136. doi:10.3233/ica-2012-0396Saldarriaga , J. G. Bernal , A. Ochoa , S. 2008 Optimized design of water distribution network enlargements using resilience and dissipated power concepts 298 312Sarma, K. C., & Adeli, H. (2000). Fuzzy Genetic Algorithm for Optimization of Steel Structures. Journal of Structural Engineering, 126(5), 596-604. doi:10.1061/(asce)0733-9445(2000)126:5(596)Sgambi, L., Gkoumas, K., & Bontempi, F. (2012). Genetic Algorithms for the Dependability Assurance in the Design of a Long-Span Suspension Bridge. Computer-Aided Civil and Infrastructure Engineering, 27(9), 655-675. doi:10.1111/j.1467-8667.2012.00780.xShafahi, Y., & Bagherian, M. (2012). A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem. Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. doi:10.1111/j.1467-8667.2012.00769.xTanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Tao, H., Zain, J. M., Ahmed, M. M., Abdalla, A. N., & Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integrated Computer-Aided Engineering, 19(1), 81-91. doi:10.3233/ica-2012-0392Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Vamvakeridou-Lyroudia, L. S., Walters, G. A., & Savic, D. A. (2005). Fuzzy Multiobjective Optimization of Water Distribution Networks. Journal of Water Resources Planning and Management, 131(6), 467-476. doi:10.1061/(asce)0733-9496(2005)131:6(467)Vitins, B. J., & Axhausen, K. W. (2009). Optimization of Large Transport Networks Using the Ant Colony Heuristic. Computer-Aided Civil and Infrastructure Engineering, 24(1), 1-14. doi:10.1111/j.1467-8667.2008.00569.xVrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., & Sorooshian, S. (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research, 39(8). doi:10.1029/2002wr001746Vrugt, J. A., Ó Nualláin, B., Robinson, B. A., Bouten, W., Dekker, S. C., & Sloot, P. M. A. (2006). Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences, 32(8), 1139-1155. doi:10.1016/j.cageo.2005.10.015Vrugt , J. A. Robinson , B. A. 2007 Improved evolutionary search from genetically adaptive multi-search method 104 3 708 11Wu , Z. Y. Wang , R. H. Walski , T. M. Yang , S. Y. Bowdler , D. Baggett , C. C. 2006 Efficient pressure dependent demand model for large water distribution system analysisXie, C., & Waller, S. T. (2011). Optimal Routing with Multiple Objectives: Efficient Algorithm and Application to the Hazardous Materials Transportation Problem. Computer-Aided Civil and Infrastructure Engineering, 27(2), 77-94. doi:10.1111/j.1467-8667.2011.00720.xXu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Zeferino, J. A., Antunes, A. P., & Cunha, M. C. (2009). An Efficient Simulated Annealing Algorithm for Regional Wastewater System Planning. Computer-Aided Civil and Infrastructure Engineering, 24(5), 359-370. doi:10.1111/j.1467-8667.2009.00594.

Graph constrained label propagation on water supply networks

In many real-world applications we have at our disposal a limited number of inputs in a theoretical database with full information, and another part of experimental data with incomplete knowledge for some of their features. These are cases that can be addressed by a label propagation process. It is a widely studied approach that may acquire complexity if new constraints in the new unlabeled data that should be taken into account are found. This is the case of the membership to a group or community in graphs. The proposal is to add the Laplacian matrix as well as another different similarity measures (may be not found in the original database) in the label propagation. A kernel embedding process together with a simple label propagation algorithm will be the main tools to achieve this approach by the use of all types of available information. In order to test the functionality of this new proposal, this work introduces an experimental study of biofilm development in drinking water pipes. Then, a label propagation through pipes belonging to a complete water supply network is approached. These pipes have their own properties depending on their network location and environmental co-variables. As a result, the proposal is a suitable and efficient way to deal with practical data, based on previous theoretical studies by the constrained label propagation process introduced.Herrera Fernández, AM.; Ramos Martinez, E.; Izquierdo Sebastián, J.; Pérez García, R. (2015). Graph constrained label propagation on water supply networks. AI Communications. 28(1):47-53. doi:10.3233/AIC-140618S475328

Municipal water demand forecasting: Tools for intervention time series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conselleria de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conseller a de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.Herrera Fernández, AM.; García-Díaz, JC.; Izquierdo Sebastián, J.; Pérez García, R. (2011). Municipal water demand forecasting: Tools for intervention time series. Stochastic Analysis and Applications. 29(6):998-1007. https://doi.org/10.1080/07362994.2011.610161S9981007296Zhou, S. ., McMahon, T. ., Walton, A., & Lewis, J. (2002). Forecasting operational demand for an urban water supply zone. Journal of Hydrology, 259(1-4), 189-202. doi:10.1016/s0022-1694(01)00582-0Bougadis, J., Adamowski, K., & Diduch, R. (2005). Short-term municipal water demand forecasting. Hydrological Processes, 19(1), 137-148. doi:10.1002/hyp.5763Jain, A., & Ormsbee, L. E. (2002). Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI. Journal - American Water Works Association, 94(7), 64-72. doi:10.1002/j.1551-8833.2002.tb09507.xPeña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2000). A Course in Time Series Analysis. Wiley Series in Probability and Statistics. doi:10.1002/9781118032978et al. 2000 . Mining Time Series of Meteorological Variables Using Rough Sets—A Case Study, Binding Environmental Sciences and Artificial Intelligent. BESAI 2000, Germany, 7:1–8.Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005McLeod, A. I., & Vingilis, E. R. (2005). Power Computations for Intervention Analysis. Technometrics, 47(2), 174-181. doi:10.1198/004017005000000094Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79. doi:10.1080/01621459.1975.10480264Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. doi:10.1016/j.ejor.2003.08.037Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214(1-4), 32-48. doi:10.1016/s0022-1694(98)00242-xWang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324(1-4), 383-399. doi:10.1016/j.jhydrol.2005.09.032Kneale , P. , See , L. , and Smith , A. 2001 .Towards Defining Evaluation Measures for Neural Network Forecasting Models; Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia.Peña, D., & Rodríguez, J. (2002). A Powerful Portmanteau Test of Lack of Fit for Time Series. Journal of the American Statistical Association, 97(458), 601-610. doi:10.1198/016214502760047122Peña, D., & Rodríguez, J. (2006). The log of the determinant of the autocorrelation matrix for testing goodness of fit in time series. Journal of Statistical Planning and Inference, 136(8), 2706-2718. doi:10.1016/j.jspi.2004.10.026LJUNG, G. M., & BOX, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. doi:10.1093/biomet/65.2.297MONTI, A. C. (1994). A proposal for a residual autocorrelation test in linear models. Biometrika, 81(4), 776-780. doi:10.1093/biomet/81.4.77

Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02)

In this paper, we present a new publicly available high-resolution daily precipitation gridded dataset developed for peninsular Spain and the Balearic islands using 2756 quality-controlled stations (this dataset is referred to as Spain02 ). The grid has a regular 0.2° (approx. 20 km) horizontal resolution and spans the period from 1950 to 2003. Different interpolation methods were tested using a cross-validation approach to compare the resulting interpolated values against station data: kriging, angular distance weighting, and thin plane splines. Finally, the grid was produced applying the kriging method in a two-step process. First, the occurrence was interpolated using a binary kriging and, in a second step, the amounts were interpolated by applying ordinary kriging to the occurrence outcomes. This procedure is similar to the interpolation method used to generate the E-OBS gridded data – the state-of-the-art publicly available high-resolution daily dataset for Europe – which was used in this study for comparison purposes. Climatological statistics and extreme value indicators from the resulting grid were compared to those from the 25 km E-OBS dataset using the observed station records as a reference. Spain02 faithfully reproduces climatological features such as annual precipitation occurrence, accumulated amounts and variability, whereas E-OBS has some deficiencies in the southern region. When focusing on upper percentiles and other indicators of extreme precipitation regimes, Spain02 accurately reproduces the amount and spatial distribution of the observed extreme indicators, whereas E-OBS data present serious limitations over Spain due to the sparse data used in this region. As extreme values are more sensitive to interpolation, the dense station coverage of this new data set was crucial to get an accurate reproduction of the extremes

Can gridded data represent extreme precipitation events?

Póster presentado en: 11IMSC - International Meeting on Statistical Climatology celebrado del 12 al 16 de Julio de 2010 en EdimburgoThe analysis and characterization of extreme precipitation at regional scale requires data at high temporal and spatial resolution due to the abrupt variations of this variable in time and space. In recent years there has been an increasing demand for comprehensive regular high-resolution (both in time and space) gridded datasets from different sectors, including hydrology, agriculture and health which are severely affected by extreme events. One of the main shortcomings of gridded datasets is that extreme events can be smoothed during the interpolation process. Heavy rainfall events can be very local and, hence, interpolation with neighboring stations may lead to an underestimation of the precipitation amounts. In this work we study the capability of a high-resolution daily precipitation gridded dataset over Spain (we refer to this dataset as Spain02, Herrera et al 2010) to characterize extreme precipitation. A dense network of 2756 quality-controlled stations was selected to develop the Spain02 grid with a regular 0.2º horizontal resolution covering the period from 1950 to 2003. We study both upper percentiles and the extreme indicators commonly used to characterize extreme precipitation regimes. We also show the performance of the gridded dataset to capture both the intensity and the spatial structure of severe precipitation episodes which constitute characteristic ephemerides of extreme weather in the Iberian peninsula. The results are compared to the 25 Km E-OBS grid (Haylock et al 2008) developed in the ENSEMBLES project, which is the best daily dataset for the whole Europe to date

Beneficial Effect of Ubiquinol on Hematological and Inflammatory Signaling during Exercise

Strenuous exercise (any activity that expends six metabolic equivalents per minute or more causing sensations of fatigue and exhaustion to occur, inducing deleterious effects, affecting negatively different cells), induces muscle damage and hematological changes associated with high production of pro-inflammatory mediators related to muscle damage and sports anemia. The objective of this study was to determine whether short-term oral ubiquinol supplementation can prevent accumulation of inflammatory mediators and hematological impairment associated to strenuous exercise. For this purpose, 100 healthy and well-trained firemen were classified in two groups: Ubiquinol (experimental group), and placebo group (control). The protocol was two identical strenuous exercise tests with rest period between tests of 24 h. Blood samples were collected before supplementation (basal value) (T1), after supplementation (T2), after first physical exercise test (T3), after 24 h of rest (T4), and after second physical exercise test (T5). Hematological parameters, pro- and anti-inflammatory cytokines and growth factors were measured. Red blood cells (RBC), hematocrit, hemoglobin, VEGF, NO, EGF, IL-1ra, and IL-10 increased in the ubiquinol group while IL-1, IL-8, and MCP-1 decreased. Ubiquinol supplementation during high intensity exercise could modulate inflammatory signaling, expression of pro-inflammatory, and increasing some anti-inflammatory cytokines. During exercise, RBC, hemoglobin, hematocrit, VEGF, and EGF increased in ubiquinol group, revealing a possible pro-angiogenic effect, improving oxygen supply and exerting a possible protective effect on other physiological alterations