A framework for 'Inclusive Multiple Modelling' with critical views on modelling practices - Applications to modelling water levels of Caspian Sea and Lakes Urmia and Van

Khatibi R., Ghorbani M. A., Naghshara S., Aydin H., Karimi V.

JOURNAL OF HYDROLOGY, cilt.587, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 587
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.jhydrol.2020.124923
  • Dergi Adı: JOURNAL OF HYDROLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Exclusive Multiple Modelling (EMM), Inclusive Multiple Modelling (IMM), Model Reuse (MR), Elastic/Plastic Model-Learning and Goal-Orientation, ARTIFICIAL-INTELLIGENCE, SUPERVISED COMMITTEE, NEURAL-NETWORK, TIME-SERIES, FLUCTUATIONS, PREDICTION, MACHINE, ALGORITHMS, ENSEMBLES, STRATEGY
  • Hacettepe Üniversitesi Adresli: Evet

Özet

A framework is formulated in this paper for data-driven modelling practices to characterise Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted information from given datasets and learning from multiple models. This can be a shift from traditional practices with the single goal of selecting a 'superior' model from multiple models without a statistical justification, which may be referred to as Exclusionary Multiple Modelling (EMM) practices. The dimensions of the framework for IMM practices are: Model Reuse (MR), Hierarchy and/or Recursion (HR), a provision of 'Elastic' model-Learning Environment (LE) and Goal-Orientation (GO) - leading to the acronym of RHEO. Proof-of-concept is presented for IMM-RHEO using three testcases: the Caspian Sea (19-years of data), Lake Urmia (50-years of data) and Lake Van (73-years of data), approx. 500 km apart. IMM practices are implemented by investigating four strategies for each testcase. The learning from the results includes: (i) the IMM strategies are capable of enhancing the accuracy of predicted water levels; (ii) the accuracy of predicting the sea-state of the Caspian Sea serves confidence building on accuracy; and (iii) the time-length of the record of Lake Van is long enough for the confidence building on the study of possible trends. IMM serves a bottom-up learning opportunity for Lake Urmia that its distressed state is due to being deprived of compensation flows without contributions from climate change. Arguably, a good management policy is the key for its restoration. IMM is at its infancy but arguably, its potential application areas are wide.