يعرض 1 - 12 نتائج من 12 نتيجة بحث عن '"Zonas regables"', وقت الاستعلام: 0.41s تنقيح النتائج
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    المؤلفون: García Vigara, Raúl

    المساهمون: Estévez Gualda, Javier, García-Marín, A.P.

    المصدر: Helvia. Repositorio Institucional de la Universidad de Córdoba
    instname

    مصطلحات موضوعية: Aridez, Clima árido, Andalucía (España), Regadío, Zonas regables

    وصف الملف: application/pdf

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    Academic Journal
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    Academic Journal
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    Academic Journal
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    Academic Journal
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    Academic Journal

    المصدر: Avances en Recursos Hidráulicos; Núm. 12 (2005); 7-20 ; Avances en Recursos Hidráulicos; No. 12 (2005); 7-20 ; 0121-5701

    وصف الملف: application/pdf

    Relation: https://revistas.unal.edu.co/index.php/arh/article/view/93119/77854; Abrahart, R.J., See, L. y Kneale, P.E., 1999. Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model, J. Hydroinformatics, 1(2), pp. 103-114. Abrahart, R.J., y See, L., 2000. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol. Process., 14, pp. 2157-2172. Abrahart, R.J., y See, L., 2002. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments, Hydrol. Earth Syst. Sci., 6(4), pp. 655-670. Anctil, E, y Rat, A., 2005. Evaluation of neural network streamflow forecasting on 47 watersheds, J. Hydrol. Eng., 10(1), pp. 85-88. Bloomfield, P., 1976. Fourier analysis of time series: an introduction, John Wiley & Sons, Nueva York. Cameron, D., Kneale, P. y See, L., 2002. An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment, Hydrol. Process., 16, pp. 1033- 1046. Chiang, Y.-M., Chang, L.-C. y Chang, F.-J., 2004. Comparison of static-feedforward and dynamic- feedback neural networks for rainfall-runoff modeling, J. Hydrol., 290, pp. 297-311. Coulbeck, B., 1988. Computer control of water supply, Leicester Polytechnic, Leicester. Cybenco, G, 1989. Approximation by superpositions of a sigmoidal function, Math. Controls, Signals, and Systems, 2: pp. 303-314. De Vries, B., y Principe, J.C. 1991. A theory for neural networks with time delays, Advances in neural information processing systems 3, Morgan Kaufmann Publishers, California. FAO, 1993. Las políticas de recursos hídricos y la agricultura, El estado mundial de la agricultura y la alimentación, Organización de las Naciones Unidas para la Agricultura, Roma. French, M.N., Krajewski, W.F., y Cuykendall, R.R., 1992. Rainfall forecasting in space and time using a neural network, J. Hydrol., pp. 137,1-31. Griñó, R., 1992. Neural networks for univariate time series forecasting and their application to water demand prediction, Neural Network World, 2(5): pp. 437-450. Gutiérrez-Estrada, J.C., De Pedro-Sanz, E., López-Luque, R., y Pulido-Calvo, I., 2004. Comparison between traditional methods and artificial neural networks for ammonia concentration forescasting in an eel (Anguilla anguilla L.) intensive rearing system, J. Aquacult. Eng., 31, pp. 183-203. Gutiérrez-Estrada, J.C., De Pedro-Sanz, E., López- Luque, R., y Pulido-Calvo, I., 2005. Estimación a corto plazo de la temperatura del agua. Aplicación en sistemas de producción en medio acuático, Ing. Agua (En prensa). Hsu, K., Gupta, H.V., y Sorooshian, S., 1995. Artificial neural network modeling of the rainfall- runoff process, Water Resour. Res., 31(10): pp. 2517-2530. Kitanidis, P.K., y Bras, R.L., 1980. Real time forecasting with a conceptual hydrological model.2: Applications and results, Water Resour. Res., 16(6): pp. 1034-1044. Kuligowski, R.J., y Barros, A.P., 1998. Experiments in short-term precipitation forecasting using artificial neural networks, Mon. Wea. Rev., 126(2), pp. 470-482. Legates, D.R., y Mccabe JR., GJ., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35(1), pp. 233-241. Lorrai, M., y Sechi, G.M., 1995. Neural nets for modelling rainfall-runoff transformations, Water Resour. Manage., 9, pp. 299-313. Martín-Del-Brío, B., y Sanz-Molina, A., 2001. Redes neuronales y sistemas borrosos, Ra-Ma, Madrid. Mason, J.C., Tem’me, A., y Price, R.K., 1996. A neural network model of rainfall-runoff using radial basis functions, J. Recherches Hydrauliques, 34(4), pp. 537-548. Moradkhani, H., Hsu, K., Gupta, H.V., y Sorooshian, S., 2004. Improved streamflow forecasting using self-organizing radial basis function artificial neural networks, J. Hydrol., 295, pp. 246-262. Nash, J.E., y Sutcliffe, J.V., 1970. River flow forecasting through conceptual models. I: A discussion of principles, J. Hydrol., 10, pp. 282-290. Ohlsson, L., 1995. Hydropolitics-conflicts over water as a development constraint, Zed Books y University Press, Londres. ONU, 1997. Comprehensive assessment of the freshwater resources of the world, United Nations Department for Policy Coordination and Sustainable Development (DPCSD), Commision on Sustainable Development. Park, H.-H., 1998. Analysis and prediction of walleye pollock (Theragra chalcogramma) landings in Korea by time series analysis, Fisheries Res., 38, pp. 1-7. Pulido-Calvo, I., Roldán, J., López-Luque, R., y Gutiérrez-Estrada, J.C., 2002. Técnicas de predicción a corto plazo de la demanda de agua. Aplicación al uso agrícola, Ing. Agua, 9(3): pp. 319-331. Pulido-Calvo, I., Roldán, J., López-Luque, R., y Gutiérrez-Estrada, J.C., 2003. Demand forecasting for irrigation water distribution systems, J. Irrig. and Drain. Eng., 129(6): pp. 422- 431. Roger, L.L., y Dowla, F.U., 1994. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling, Water Resour. Res., 30(2), pp. 457-481. Rumelhart, D.E., Hinton, G.E., y Williams, R.J., 1986. Learning representations by backpropagation errors, Nature, 323(9): 5pp. 33-536. Saporta, D., y Muñoz, M., 1994. El consumo en redes de distribución. Predicción diaria de la demanda, Mejora del rendimiento y de la fiabilidad en sistemas de distribución de agua, Cabrera y Vela (eds.), Universidad Politécnica de Valencia, Valencia. See, L., y Openshaw, S., 2000. Ahybrid multi-model approach to river level forecasting, Hydrol. Sci. J., 45(4), pp. 523-536. Shepherd, A. J., 1997. Second-Order Methods for Neural Networks, Springer, Nueva York. Shin, H-S., y Salas, J.D., 2000. Regional drought analysis based on neural networks, J. Hydrol. Eng., 5(2), pp. 145-155. Sumpsi, J.M., Garrido, A., Blanco, M., Varela, C., y Iglesias, E., 1998. Economía y política de gestión del agua en la agricultura, Ministerio de Agricultura, Pesca y Alimentación y Ed. Mundi- Prensa, Madrid. Tan, Y., y Van Cauwenberghe, A., 1999. Neural- network-based d-step-ahead predictors for nonlinear systems with time delay, Eng. Applic. Artif. Intell., 12(1), pp. 21-25. Thirumalaiah, K., y Deo, M.C., 1998. River stage forecasting using artificial neural networks, J. Hydrol. Eng., 3(1), pp. 26-32. Thirumalaiah, K., y Deo, M.C., 2000. Hydrological forecasting using neural networks, J. Hydrol. Eng., 5(2), 180-189. Tokar, A.S., y Johnson, P.A., 1999. Rainfall-runofF modeling using artificial neural networks, J. Hydrol. Eng., 4(3), pp. 232-239. Tokar, A.S., y Markus, M. 2000. Precipitation-runoff modeling using artificial neural networks and conceptual models, J. Hydrol. Eng., 5(2), pp. 156- 161. Tsoukalas, L.H., y Uhrig, R.E., 1997. Fuzzy and neural approaches in engineering, Wiley Interscience, Nueva York. Ventura, S., Silva, M., Pérez-Bendito, D., Hervás, Y.C., 1995. Artificial neural networks for estimation of kinetic analytical parameters, Anal. Chem., 67(9): pp. 1521-1525. Ventura, S., Silva, M., Pérez-Bendito, D., Hervás, Y.C., 1997. Computational neural networks in conjunction with principal component analysis for resolving highly nonlinear kinetics, J. Chem. Inf. Comput. Sci., 37(2): pp. 287-291. Yang, C.C., Prasher, S.O., Lacroix, R., Sreekanth, S., Patni, N.K., y Masse, L., 1997. Artificial neural network model for subsurface-drained farmland, J. Irrig. Drain. Eng., 123(4), pp. 285-292. Zhang, M., Fulcher, J. y Scofield, R.A. 1997. Rainfall estimation using artificial neural network group, Neurocomputing, 16, pp. 97-115.; https://revistas.unal.edu.co/index.php/arh/article/view/93119

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    المصدر: UPCommons. Portal del coneixement obert de la UPC
    Universitat Politècnica de Catalunya (UPC)
    Ingeniería del agua 10 (4), 517-526 (2003)
    Helvia. Repositorio Institucional de la Universidad de Córdoba
    instname
    RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia

    وصف الملف: application/pdf; p. 9

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    Electronic Resource
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    Electronic Resource