A Hybrid Flood Forecasting Approach Using Hidden Markov Model and Machine Learning Techniques

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. The amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. Machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. The proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. The developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August.