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Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks

机译:摩洛哥西南部的屋顶露水,雨雾收集和使用神经网络的预测露水建模

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Two coastal sites were investigated in an arid region of southwest Morocco to determine the amount of dew, fog and rain that could be collected from rooftops for household use. Systematic measurements were performed in Mirleft (43m asl, 200m from the coast) for 1year (May 1, 2007 to April 30, 2008) and in Id Ouasskssou (240m asl, 8km from the coast) for three summer months (July 1, 2007 to September 30, 2007). Dew water was collected using standard passive dew condensers and fog water by utilizing planar fog collectors. The wind flow was simulated on the rooftop to establish the location of the fog collector. At both sites, dew yields and, to a lesser extent, fog water yields, were found to be significant in comparison to rain events. Mirleft had 178 dew events (48.6% of the year, 18±2Lm ~(-2) cumulated amount) and 20 fog episodes (5.5% of the year, 1.4Lm ~(-2) with uncertainty -0.2/+0.4Lm ~(-2) cumulated amount), corresponding to almost 40% of the yearly rain contribution (31 rain events, 8.5% of the year, 49±7mm cumulated amount). At Id Ouasskssou there were 50 dew events (7.1±0.3Lm -2, 54.3% frequency), 16 fog events (6.5Lm ~(-2) with uncertainty -0.1/+1.8Lm ~(-2), 17.4% frequency) and six rain events (16±2mm, 6.5% frequency).Meteorological data (air and dew point temperature and/or relative humidity, wind speed and wind direction, cloud cover) were recorded continuously at Mirleft to assess the influence of local meteorological conditions on dew and fog formation. Using the set of collected data, a new model for dew yield prediction based on artificial neural networks was developed and tested for the Mirleft site. This model was then extrapolated to 15 major cities in Morocco to assess their potential for dew water collection. It was found that the location of the cities with respect to the Atlas mountain chain, which controls the circulation of the humid marine air, is the main factor that influences dew production.
机译:在摩洛哥西南部的干旱地区对两个沿海地点进行了调查,以确定可以从屋顶收集的露水,雾和雨量,以供家庭使用。在三个左月(2007年7月1日)在Mirleft(43m asl,距海岸200m)进行了为期1年(2007年5月1日至2008年4月30日)和在Id Ouasskssou(240m asl,距海岸8 km)进行了系统测量。至2007年9月30日)。使用标准的被动式冷凝器收集露水,并使用平面除雾器收集雾水。在屋顶上模拟了风流,以确定集雾器的位置。与降雨事件相比,在这两个地点的露水产量以及在较小程度上的雾水产量都被认为是重要的。 Mirleft发生了178次露水事件(占年48.6%,累积量为18±2Lm〜(-2))和20次雾霾发作(占年占5.5%,1.4Lm〜(-2)且不确定度为-0.2 / + 0.4Lm〜 (-2)累积量),几乎相当于年度降雨贡献的40%(31次降雨事件,占年度的8.5%,累积量为49±7mm)。 Id Ouasskssou发生了50次露水事件(7.1±0.3Lm -2,54.3%频率),16次起雾事件(6.5Lm〜(-2),不确定度-0.1 / + 1.8Lm〜(-2),17.4%频率)六次降雨事件(16±2mm,频率6.5%)。在Mirleft连续记录了气象数据(空气和露点温度和/或相对湿度,风速和风向,云量),以评估当地气象条件的影响。在露水和雾气形成。使用收集的数据集,开发了基于人工神经网络的露水产量预测新模型,并针对Mirleft站点进行了测试。然后将该模型外推到摩洛哥的15个主要城市,以评估其收集露水的潜力。人们发现,城市相对于阿特拉斯山脉的位置,控制着潮湿的海洋空气的流通,是影响露水生产的主要因素。

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