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Flood is a common and critical environmental challenge with terrible consequences for humanity and civilization. An effective and consistent system for flood monitoring is required to mitigate its devastating impacts. This study presents an energy-conserving cloud-assisted system for flood detection and vulnerability assessment. The system utilizes a Bayesian belief network (BBN) to detect flood events. The optimization-based adaptive neuro fuzzy inference system (ANFIS) is used as a vulnerability analysis component at the cloud layer for flood monitoring and forecasting. According to the findings, ANFIS-GOA (grasshopper optimization algorithm) provides the best values for: Willmott index (WI), 0.98322 and 0.96458; mean absolute error (MAE), 0.00138 and 0.00146; root mean square error (RMSE), 0.01953 and 0.01754 for the training phase; and WI index, 0.97251 and 0.98516; MAE, 0.00015 and 0.00042; RMSE, 0.0024 and 0.00193 for the testing phase for Kottayam and Idukki regions, respectively.

ANFIS-based flood detection and vulnerability assessment framework