Affiliations: [a] University School of Information, Communication and Technology, Gobind Singh Indraprastha Universty, Delhi, India | [b] Bharati Vidyapeeth’s College of Engineering, New Delhi, India | [c] Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, Delhi, India | [d] Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA, USA
Correspondence:
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Corresponding author: Nikita Jain, University School of Information, Communication and Technology, Gobind Singh Indraprastha Universty, Delhi, India. E-mail: [email protected].
Abstract: Tsunami occurrence has created havoc for humans. This unfortunate event however does provide some pre-alert signals which if carefully analyzed can help in pre-sensing this havoc. The signals can be in form of certain indicators in environment and in nature which can be sensed dynamically. Various studies have reported cases of animal behavioral signals prior to tsunami. These changes are a result of environmental changes before any such event occurrence. This paper proposes a confident fuzzified system derived over marine behavior to predict tsunamis (3CCCOVFTP). The system uses an overlap function based linguistic rule derived algorithm creating three class of classifications. Using this system, aquatic behavior is studied, analyzed and classified to produce alerts in real time. Initially, the behavioral attributes are identified for both turtle and earthworm dataset which do produce anomalous signals. The attributed data is further analyzed to retrieve fuzzy rules using which alert, pre-alert or no alert overlapped opinions are extracted. The dataset for analysis includes the following derived parameters: adaptive electromagnetic field, Underwater count of particular specie Deviation in angle of motion (calculated for both sea turtles and earthworms defining their navigation activity per month and per day). The proposed system generates criteria three class categorization on the basis of the derived inputs. From the result obtained, confidence level for each extracted rule is formulated to derive optimized rule set that can serve mathematical formulation to in time alert generation. For prediction of any similar activity in the coming years, 2004 has been utilized as the baseline opinion year with resulting constraint as default rule. The tertiary classification formulated using the proposed algorithm classifies the behavior into three alert categories: Alert, Pre-Alert and No-Alert. Based on the quantified confident opinions, alerts primarily based on aquatic animal behavior can be generated for future years.
