Automated classification of an environmental sensitivity index

Environmental Sensitivity Indices (ESI) composed of many field-data are essential for monitoring and control systems. At the beginning of the last decade an ESI of the German Wadden Sea was developed for use by the relevant authorities. This ESI was derived by experts semi-manually analysing the extensive field data-set. An algorithm is presented here which emulates human expert-decisions on the classification of sensitivity classes. This will permit the necessary regular updates of ESI-determination when new field data become available using automated classifications procedures. After tuning the algorithm parameters it generates decisions identical to those of human experts in about 97% of all locations tested. In addition, the algorithm presented also enables erroneous or extremely seldom field data to be identified.