India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into orientation in the farming sector to decide the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and the Hadoop file system. In the proposed model (EMLRM) first, we stored the unstructured weather data in hadoop distributed file system (HDFS), process that stored data by using MapReduce Algorithm and build the rainfall prediction model by utilizing Multiple Linear Regression.We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. The experimental outcomes show that the EMLRM provided the lowest value of Root Mean Square Error (RMSE= 0.274) and Mean Absolute Error (MAE= 0.0745) compared with existing methods. The results of the analysis will help the farmers to adopt effective modeling approach for predicting long-term seasonal rainfall.

No abstract

Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.

No abstract

No abstract