Artificial neural networks in underground infrastructure management - Dehghan, Shahide;Norouzi, Hoosein;Gholami, Hossein
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Due to the decrease in rainfall and drought in the last decade and as a result of the lack of water in a wide area of the country, groundwater management is very important and sensitive. In order to apply a correct management, the need to identify and model and predict the fluctuations of the water table in the plains for long-term planning and more and better use of the water potentials of the plains is deeply felt. Various factors and factors affect the level of underground water, among them are weather factors (temperature, rainfall, evaporation), the amount of discharge and feeding from…mehr

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Produktbeschreibung
Due to the decrease in rainfall and drought in the last decade and as a result of the lack of water in a wide area of the country, groundwater management is very important and sensitive. In order to apply a correct management, the need to identify and model and predict the fluctuations of the water table in the plains for long-term planning and more and better use of the water potentials of the plains is deeply felt. Various factors and factors affect the level of underground water, among them are weather factors (temperature, rainfall, evaporation), the amount of discharge and feeding from the table, etc., which make the analysis of this phenomenon difficult. formation Physical-conceptual models, regression and time series are the most common methods of analyzing the fluctuations of the underground water level (hydrograph).
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Autorenporträt
Dr. Shahide Dehghan, Ph.D., Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Technical Skills are Climatology, Statistical Analysis, Geography, Geo-Natural Hazards, Geomorphology, Factor Analysis, Data Analysis, Climate Change, Atmosphere, Global Warming, Climate Sciences, Insurance Investment, Water Resources.