{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T11:53:53Z","timestamp":1648900433381},"reference-count":0,"publisher":"Sciedu Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIR"],"abstract":"<jats:p>The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted\u00a0pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak\u00a0detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification\u00a0(DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset\u00a0comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet\u00a0pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid\u00a0learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities.\u00a0ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between\u00a0the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing\u00a0error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made\u00a0with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and\u00a0localization of PPP activities.<\/jats:p>","DOI":"10.5430\/air.v6n2p39","type":"journal-article","created":{"date-parts":[[2017,4,6]],"date-time":"2017-04-06T22:19:37Z","timestamp":1491517177000},"page":"39","source":"Crossref","is-referenced-by-count":1,"title":["Discrete event based hybrid framework for petroleum products pipeline activities classification"],"prefix":"10.5430","volume":"6","author":[{"given":"S. S.","family":"Udoh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"O. C.","family":"Akinyokun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U. G.","family":"Inyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"O.","family":"Olabode","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. B.","family":"Iwasokun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3394","published-online":{"date-parts":[[2017,4,6]]},"container-title":["Artificial Intelligence Research"],"original-title":[],"link":[{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/9465\/6956","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/9465\/6956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,4,6]],"date-time":"2017-04-06T22:19:37Z","timestamp":1491517177000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/view\/9465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,6]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,2,27]]}},"URL":"https:\/\/doi.org\/10.5430\/air.v6n2p39","relation":{},"ISSN":["1927-6982","1927-6974"],"issn-type":[{"value":"1927-6982","type":"electronic"},{"value":"1927-6974","type":"print"}],"subject":[],"published":{"date-parts":[[2017,4,6]]}}}