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Abstract:In this study, we analysed the influence of the North Atlantic Oscillation (NAO) on the hydrological response of the Duero River (central Spain) and its tributaries. We identified the positive and negative phases of the winter NAO for the period 1961-2006 and related precipitation and river discharge anomalies. Significant differences in precipitation and river discharge were found between the positive and negative NAO phases with negative anomalies (dry conditions) during positive NAO periods, and positive anomalies (wet conditions) during negative NAO periods. Marked differences were also found in the precipitation and river discharge response times to the NAO episodes, with an immediate response for precipitation but a lag and more sustained response for river discharges. Differing patterns were also identified in the response of river discharge to the NAO throughout the Duero basin. The physical characteristics of watersheds (including area, altitude and permeability explained most of the differences in the timing and magnitude of anomalies in the river discharge in response to the NAO. The findings highlight the great variability in the hydrological response of rivers to the NAO episodes.

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For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and EnergyAware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.

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