NDLI: Research on factors analysis model of dualistic soil salinization sensitivity in typical northwestern arid area

Content Provider	IEEE Xplore Digital Library
Author	Tao Sun Shifeng Huang Shibing Pan Haiying Deng
Copyright Year	2007
Description	Author affiliation: China Inst. of Water Resources & Hydropower Res., Beijing (Tao Sun; Shifeng Huang; Shibing Pan)
Abstract	Soil secondary salinization is one of the typical ecological side effects caused by land and water resources development in northwestern arid China. Factors that affect the occurrences and developments of salinization come from both natural conditions and human activities. Research on the mechanisms of salinization, build dynamic prediction model of salt accumulation and analyze sensitivities to different factors would supply effective references to the prediction and prevention of soil salinization. It is well known that related factors are always intertexture together, affecting each other, which result in multivariable, nonlinear and overall influences that work on the process of soil salinization. Artificial intelligence technologies may play important role in this domain. In this paper, genetic artificial neural network based model is built to simulate and evaluate soil salt accumulation and sensitivity of soil salinization. Example is taken from the Shule River watershed, typical arid area in northwestern China. Basic data of June 2000 are prepared depending on GIS and Remote Sensing. Precipitations, evaporations, groundwater levels, groundwater chemical analysis data and soil accumulation data are achieved and interpolated in the research area. Slope of the land are derived from DEM, MODIS images are used in the process of dealing with land use information. At the same time, landform and soil type are considered in model building. Soil salt accumulation is analyzed with its 8 influenced factors with verified models. Results showing that groundwater TDS is the most sensitive factor followed by groundwater level, evaporation and the depth of upper bed of clay. In most cases clay layers play key roles in soil salt accumulation, precipitation and slop have similar sensitivities. Results would have better research and application value in arid areas of northwestern China.
Starting Page	3138
Ending Page	3141
File Size	883813
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Soil Biological system modeling Water resources Humans Predictive models Artificial intelligence Genetics Artificial neural networks Rivers Geographic Information Systems shule river watershed soil secondary salinization affective factors sensitivity analysis RS and GIS genetic artificial neural network
Content Type	Text
Resource Type	Article

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