An Intillegent Decision Making System for Flood Prediction

This paper presents a prediction model for flood using wireless sensor networks (WSN). The sensor nodes are deployed at optimal locations using the cuckoos search algorithm in the flood prone area. The resulting data is collected to forecast the occurrence of flood in river banks using the data mining algorithms. The parameters used for the prediction of flood are temperature, humidity, soil moisture, water level, rainfall [Victor Seal, 2012]. Based on these data, we have generated the decision tree using the C5.0 and genetic algorithm for efficient forecasting of flood that takes efficient decision and generate suitable warnings message to the concerned authority, such that they can take the precautionary measures to reduce the damage caused by the flood. The results of the proposed algorithm are compared with some existing techniques to evaluate the performance and the proposed technique proofs better results.