Today, India is one of the worst flood-affected countries in the world, with the recent disaster in Kerala in August 2018 being a prime example. A good amount of work has been carried out by employing Internet of Things (IoT) and machine learning (ML) techniques in the past for flood occurrence based on rainfall, humidity, temperature, water flow, water level etc. However, the challenge is that no one has attempted the possibility of occurrence of flood based on temperature and rainfall intensity. So accordingly Deep Neural Network has been employed for predicting the occurrence of flood based on temperature and rainfall intensity. In addition, a deep learning model is compared with other machine learning models (support vector machine (SVM), K-nearest neighbor (KNN) and Naïve Bayes) in terms of accuracy and error. The results indicate that the deep neural network can be efficiently used for flood forecasting with highest accuracy based on monsoon parameters only before flood occurrence.

The growing demand for energy and the increasing carbon footprint in the globe has made electricity utilities to move from nonrenewable energy to renewable energy. The integration of renewables into the electric grid is increasing day-by-day. The consumers’ energy consumption needs to be managed wisely and effectively. The Internet of Things has helped in connecting all homes and appliances to the Internet. With smart homes, it is possible to study consumer’s usage patterns and their demand for energy. During peak hours of the day, the demand for energy increases and have to be met by the utilities by starting up additional coal-fired generation. This makes peak hour usage of electricity costly. This paper studies the usage behavior of consumers from their historical data and predicts the demand for energy every hour for the individual consumer for the next 72 h using time series analysis. Also, the work statistically studies the usage pattern of appliances in every home thereby finding which appliances play a significant role during the peak hour usage. This paper will help utilities understand how their consumers use electricity and can encourage consumers to shift usage of peak hour appliances to nonpeak hours. Also, consumers can grant control of individual appliances to utilities, to curtail the load during peak hours to reduce the demand.