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Distributed storage and Distributed prediction method for flash flood forecasting disaster forecasting system of Rainfall data is researched. Focused on the rapid growth of the collected Hydrological data and the demands for prediction accuracy and timeliness of forecasts is increasing, respectively used Hadoop distributed file system to store data and use MapReduce framework and the genetic algorithm to optimize the number of hidden layer nodes and the weights as well as the thresholds of the network to predict data. Based on multi-factor flash flood disaster rainfall BP neural network prediction model, combining the characteristics of genetic algorithm can achieve global optimization to optimize the number of hidden layer nodes and the weights as well as the thresholds of the network,and in the procedure of data parallel processing adopted the way of batch mode and MapReduce workflow, and used the error and the accuracy to evaluate the prediction model,which solve the problem of network training time when the neural network in dealing with mass data. Experiments show that this method can greatly reduce the running time without affecting the accuracy, and improve prediction efficiency.