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@article{159902,
        author = {Unnati Bokade  and Pallavi Bangare and Mayur Dhamankar and Chetan Dhawale and Dr. S. W. Mohod},
        title = {Evaluation of the Flood Forecasting Capability of a Machine Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {684-690},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159902},
        abstract = {Predicting floods involves forecasting future levels of water or runs at one or more areas along a river system over a given time period. Flood control measures necessitate precise and consistent forecasting in order to plan, implement, and rehab. In spite of problems with data scarcity, soft computing technique-based models for operational flood forecasting systems are frequently better in terms of accuracy and dependability. When a significant amount of water overflows onto a plot of land, flooding occurs. Based on water level or discharges from hydraulic structures, the flood forecasting (FF) system will give an advisory. In our project we have collect kerela dataset based on kaggle.com website. Then we have to apply preprocessing technique then cleaning the null values from the dataset. Then data can be split into two dataset that is training and testing dataset. we have to used training and testing techniques to analyse the dataset and to identify final accuracy and improve model performance then we have to show the results that are flood may happen or flood may not happen.  },
        keywords = {Rainfall, SVM classifier, Naïve Bayes  classifier, Decision tree, KNN classifier.},
        month = {},
        }