Knowledge discovery — A control theory perspective

Foss, Bjarne A.

doi:10.1007/3-540-63223-9_100

Cited by 3 publications

(1 citation statement)

References 8 publications

(3 reference statements)

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“…The effectiveness of an oil spill response system and the robustness of a recovery plan are highly contingent upon an IT infrastructure that enhances information management, cooperation and coordination during disasters [3,4] . Computer Systems, Decision Support Systems, KD and DM are some core tools that can assist in many aspects of oil spillage risk management.…”

mentioning

confidence: 99%

Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

Akinyokun

Inyang²

2013

AIR

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mentioning

confidence: 99%

Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

Akinyokun

Inyang²

2013

AIR

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Implementation of a Hybridized Machine Learning Framework for Flood Risk Management

Akinyokun

Inyang

Akpan

2019

Advances in Intelligent Systems and Computing

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A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification

Inyang

Akpan

Akinyokun

2020

Int. J. Comp. Intel. Appl.

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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’ 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.

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Knowledge discovery — A control theory perspective

Cited by 3 publications

References 8 publications

Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

Implementation of a Hybridized Machine Learning Framework for Flood Risk Management

A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification

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