Abstract
Watersheds are featured by a variety of hydrological, meteorological, and ecological characteristics. Complexity and uncertainty are usually two major challenges during watershed classification which is one of the key processes in hydrological modeling. This study aims to develop an integrated rule-based fuzzy adaptive resonance theory mapping (IRFAM) system, by incorporating fuzzification and rule-based operation to more efficiently handle the complexity and uncertainty. The developed system has been tested with a case study conducted in the Deer River watershed in Manitoba, Canada. The results are further compared with the ones generated by the conventional adaptive resonance theory mapping (ARTMap) method. All subbasins are classified by IRFAM while ARTMap leaves five subbasins unclassified. Furthermore, another nine subbasins in the juncture between classified groups from the ARTMap classification results are relocated by IRFAM. The IRFAM system can take advantage of fuzzy set theory to generate full ...
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