caa a22 4500 467902828 CHVBK 20180406152823.0 cr unu---uuuuu 170328e20060601xx s 000 0 eng 10.1007/s10661-006-8498-0 doi (NATIONALLICENCE)springer-10.1007/s10661-006-8498-0 The Parameter Optimization in the Inverse Distance Method by GeneticAlgorithm for Estimating Precipitation [Elektronische Daten] [Chia-Ling Chang, Shang-Lien Lo, Shaw-L Yu] The inverse distance method, one of the commonly used methods for analyzing spatial variation of rainfall, is flexible if the order of distances in the method is adjustable. By applying the genetic algorithm (GA), the optimal order of distances can be found to minimize the difference between estimated and measured precipitation data. A case study of the Feitsui reservoir watershed in Taiwan is described in the present paper. The results show that the variability of the order of distances is small when the topography of rainfall stations is uniform. Moreover, when rainfall characteristic is uniform, the horizontal distance between rainfall stations and interpolated locations is the major factor influencing the order of distances. The results also verify that the variable-order inverse distance method is more suitable than the arithmetic average method and the Thiessen Polygons method in describing the spatial variation of rainfall. The efficiency and reliability of hydrologic modeling and hence of general water resource management can be significantly improved by more accurate rainfall data interpolated by the variable-order inverse distance method. Springer Science + Business Media, Inc., 2006 genetic algorithm nationallicence inverse distance nationallicence precipitation interpolation nationallicence rainfall nationallicence spatial variation nationallicence watershed hydrology nationallicence Chang Chia-Ling Graduate Institute of Environmental Engineering, National Taiwan University, 106, Taipei, Chinese Taiwan aut Lo Shang-Lien Graduate Institute of Environmental Engineering, National Taiwan University, 106, Taipei, Chinese Taiwan aut Yu Shaw-L Department of Civil Engineering, University of Virginia, Charlottesville, Virginia, USA aut Environmental Monitoring and Assessment Kluwer Academic Publishers 117/1-3(2006-06-01), 145-155 0167-6369 117:1-3<145 2006 117 10661 https://doi.org/10.1007/s10661-006-8498-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10661-006-8498-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chang Chia-Ling Graduate Institute of Environmental Engineering, National Taiwan University, 106, Taipei, Chinese Taiwan aut NATIONALLICENCE 700 1- Lo Shang-Lien Graduate Institute of Environmental Engineering, National Taiwan University, 106, Taipei, Chinese Taiwan aut NATIONALLICENCE 700 1- Yu Shaw-L Department of Civil Engineering, University of Virginia, Charlottesville, Virginia, USA aut NATIONALLICENCE 773 0- Environmental Monitoring and Assessment Kluwer Academic Publishers 117/1-3(2006-06-01), 145-155 0167-6369 117:1-3<145 2006 117 10661 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer