Modeling catchment sediment yield: a genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Garg:2014:NH,

• author = "Vaibhav Garg",
• title = "Modeling catchment sediment yield: a genetic programming approach",
• journal = "Natural Hazards",
• year = "2014",
• volume = "70",
• number = "1",
• pages = "39--50",
• month = jan,
• email = "vaibhav@iirs.gov.in",
• keywords = "genetic algorithms, genetic programming, Sediment yield, Modelling and simulation, Evolutionary technique, Soft computing",
• publisher = "Springer",
• ISSN = "0921-030X",
• language = "English",
• URL = "http://link.springer.com/article/10.1007%2Fs11069-011-0014-3#page-1",
• DOI = "doi:10.1007/s11069-011-0014-3",
• size = "12 pages",
• abstract = "Hydrologic processes are complex, non-linear, and distributed within a watershed both spatially and temporally. One such complex pervasive process is soil erosion. This problem is usually approached directly by considering the sediment yield. Most of the hydrologic models developed and used earlier in sediment yield modelling were lumped and had no provision for including spatial and temporal variability of the terrain and climate attributes. This study investigates the suitability of a recent evolutionary technique, genetic programming (GP), in estimating sediment yield considering various meteorological and geographic features of a basin. The Arno River basin in Italy, which is prone to frequent floods, has been chosen as case study to demonstrate the GP approach. The results of the present study show that GP can efficiently capture the trend of sediment yield, even with a small set of data. The major advantage of the GP analysis is that it generates simple parsimonious expression offering some possible interpretations to the underlying process.",

}

Genetic Programming entries for Vaibhav Garg

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