مشخصات پژوهش

صفحه نخست /Groundwater vulnerability ...
عنوان Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)
نوع پژوهش مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها Ardabil aquifer; Artificial intelligence models; Nitrate; Supervised Intelligent Committee Machine; Vulnerability index
چکیده This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that they are able to estimate groundwater vulnerability and can cope with heterogeneity and uncertain parameters and SICM indeed improves on the performance of its constituent AI models.
پژوهشگران عطاالله ندیری (نفر اول)، مریم قره خوانی (نفر دوم)، رحمان خطیبی (نفر سوم)، سینا صادق فام (نفر چهارم)، اصغر اصغری مقدم (نفر پنجم)