Forecasting of Precipitation in India by Different Data Types using Investigation of Radial Basis Function Neural Network Model
TL;DR: In this paper, the Radial Basis Function Neural Network Model with back propagation algorithm was used to forecast the predication of rainfall for the state of Punjab, India in 2015.
Abstract: There is a rainy season occurs during the period from June to august in almost all geographical parts of India. Moreover, some of the states like Uttarakhand, Cherapunji, Mumbai, Tamil Nadu etc. may suffer from some natural disaster. If we early predict such misfortunes through the variety of big data collected for such distinct positions at a particular amount of time then certainly can save the life and goods from such big natural calamities. Such normalized data can be updated at a regular interval of time. In view of this, the time series data analysis provides a method to early aware and protects the life of people from such natural disasters. The proposed method exploited the use the Radial Basis Function Neural Network Model with back propagation algorithm to make compatible with time series data analysis to forecast the predication of rainfall for the state of Punjab, India. In this technique, two types of predictions are used which are based on fifteen and twenty days. The comparison results reveal those fifteen days prediction provides more effective classification accuracy than twenty days prediction.
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28 Apr 2022
TL;DR: An automatic model for crisp weather forecasting which may be considerate to the people, so that they can take precautions earlier and produce better classification accuracy than 7 days' prediction.
Abstract: The winter season starts suddenly in the mid of the Dew and ends in Spring. If we will analyze in a better way, the cold starts in the mid of October and continue up to mid of February month. Generally, some the states like Jammu, Kashmir, Uttar Pradesh, Uttarakhand, Punjab etcetera the people are suffering from a heavy cold. At that time the temperature is as much as decreased so that the old generation people cannot tolerate it. Sometimes also the meteorological department is very busy predicting temperature because in India other natural disasters like floods, droughts, cyclones etcetera are also coming suddenly. So in this investigation work, we have designed an automatic model for crisp weather forecasting which may be considerate to the people, so that they can take precautions earlier. For this work, we have worked for Odisha state for the investigation purpose which uses barometric parameters as well as the machine learning techniques like Support Vector Machine along with Random Forest for our investigation work. The dataset that we have used is IMD which is used for both training and testing. In this model, we have used two prognoses which are based on five and seven days respectively. The contrast result babbles 5 days produce better classification accuracy than 7 days' prediction.
5 citations
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TL;DR: In this paper, three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree), are investigated for forecasting and predicting of monthly streamflows.
Abstract: Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly streamflows in both stages of the study.
94 citations
01 Jan 2017
TL;DR: A comparative analysis was performed on the basis of advantages, disadvantages and methodology applied by various techniques for detecting driver's behavior for Intelligent Transportation Systems (ITS).
Abstract: Driver behavior is an essential component of the driver-vehicle-environment system and plays a key role in the design of the transport and vehicle systems in order to improve the efficiency and safety of human agility. The most important factors that influence driver behavior are the environment, vehicle and the driver itself. Experience, distraction, fatigue, drowsiness etc. are so me of the other factors that have an impact on driver behavior. Improper driving behavior is the leading cause of the accidents and thus, detection of driver behavior is an emerging area of research interest. This paper discusses the various techniques used for monitoring driver behavior and also classifies them into real-time and non-real time techniques. A comparative analysis was performed on the basis of advantages, disadvantages and methodology applied by various techniques for detecting driver's behavior for Intelligent Transportation Systems (ITS).
87 citations
01 May 2020
TL;DR: Various forecasting algorithmic approaches are compared and explored and their limitations and usefulness for different types of time series data in different domains are explored.
Abstract: Time series data abound in many realistic domains. The proper study and analysis of time series data help to make important decisions. Study of such data is very useful in many applications where there are trendy changes with time or specific seasonality as in electricity demand, cloud workload, weather and sales, cost of business products, etc. By understanding the nature of the time series and the objective of analysis, we have used different approaches to learn and extract meaningful information that can satisfy the business needs. The present paper covers and compares various forecasting algorithmic approaches and explores their limitations and usefulness for different types of time series data in different domains.
37 citations
01 Apr 2020
31 citations
TL;DR: In this paper, the authors performed an extensive state-of-the-art on the techniques and methods used for recognizing and classifying HMSE, and brought out all significant findings in sub-processes, representation models, algorithms, tools, datasets, and comparative analysis of the accuracy of the recognition models.
26 citations