{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T11:53:49Z","timestamp":1778327629288,"version":"3.51.4"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:p>Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. [Formula: see text]-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts\u2019 judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.<\/jats:p>","DOI":"10.1142\/s1469026820500121","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T09:02:04Z","timestamp":1594976524000},"source":"Crossref","is-referenced-by-count":44,"title":["A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification"],"prefix":"10.1142","volume":"19","author":[{"given":"Udoinyang Godwin","family":"Inyang","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Uyo, Uyo, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emem Etok","family":"Akpan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Polytechnic, Nassarawa, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oluwole Charles","family":"Akinyokun","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Federal University of Technology, Akure, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"S1469026820500121BIB001","doi-asserted-by":"publisher","DOI":"10.1891\/0739-6686.30.149"},{"key":"S1469026820500121BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2006.02.016"},{"key":"S1469026820500121BIB003","first-page":"17","volume-title":"Encyclopedia of data warehousing and mining, Information Science Reference","author":"Yao J. 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