An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis

Krishna, G. Vamsi

doi:10.5120/21273-4093

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…Fahad Sheikh, S. Karthick, D. Malathi, J. S. Sudarsan, and C. Arun [3] has discussed C4.5 and Na¨ıve Bayes algorithm, a novel strategy for foreseeing the kinds of weather dependent on the PV power data and partial meteorological data was discussed by Wenying Zhang, Huaguang Zhang, Fellow, IEEE, Jinhai Liu, Kai Li, Dongsheng Yang, and Hui Tian [4]. G.Vamsi Krishna [5] using the autoregressive integrated moving average (ARIMA) model to estimate the future worth. In this model, at first, a numerical model is produced by thinking about an ordered group of data, and afterward, the forecast is completed by using the model utilizing the current qualities and the past information.…”

Section: Related Workmentioning

confidence: 99%

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

2021

IJETER

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Forecast of future weather is one of the dominant concerns in today’s era. Several techniques that have been used to forecast future weather are statistical analysis, machine learning, and deep learning techniques. Forecast of weather can assist in the decision-making process for the prevention of disasters. While forecasting weekly weather can help people, decide and manage their activities and recreational programs it can also help Smart management programs. The accuracy of the Short term ambient forecast conditions is particularly conducive to the development of forecast management strategies. This paper presents an inclusive survey on such methods based on Machine Learning, and Deep Learning Techniques used to predict weather and discusses the applications, types, and problems of the forecasting.

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Section: Related Workmentioning

confidence: 99%

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

2021

IJETER

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“…Comparative results between the three sets of classifiers, NDFD, and MLR models for this study were integrated homogeneously, the best performers were a function of prediction hour, domain, and feature selection technique. Krishna (2015) developed weather forecasting method using Data Mining and Forecasting Analysis. Weather data was considered with attributes comprising of wind pressure, humidity, Minimum and Maximum Temperature, Forecast and Type of Visakhapatnam city for a period of 97 days.…”

Section: Weather Forecastingmentioning

confidence: 99%

Smart Energy Harvesting and Severe Weather Alert System

Mirabueno¹,

Mendoza²,

Lopez³

2019

J BIMP-EAGA RD

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Life in the rural coastal communities in the Philippines depicts poverty. This poverty is due to the lack of direct energy access and frequent destruction of livelihood lead by severe weather such as storms and typhoons. To help these communities to become sustainable the researchers provided a solution to enable alternative energy access to these communities as well as severe weather early warning. This research implements a hybrid alternative energy harvesting system using an attachable solar and wind energy apparatus for community use;; farther--time early warning for severe weather through weather APIs with a real--time atmospheric analysis and prediction for a short--time early warning;; and a handheld lantern for fishermen that allows them to receive farther and short--time alerts. The system is integrated into The Things Network through LoraWAN™ which allows administrators to monitor the energy stored and weather conditions as well as communicating with the lanterns at a longer and more reliable network.

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“…Weather forecasts provide critical information about future weather. There are various techniques involved in weather forecasting, from relatively simple observation of the sky to highly complex computerized scientific models (M. Tektas, 2010) [6].An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis by G.Vamsi Krishna where in this paper the weather data was considered with attributes, such as wind pressure, humidity, Temperature, Forecast and Type, of Visakhapatnam city for a period of 97days. The forecasting experiment was carried out for test, the weather condition for the following 15 days by enabling the ARIMA model to predict the forecasts [7].…”

Section: A Definationmentioning

confidence: 99%

“…Weather Forecasting is an inspiration about future weather. There are various techniques concern with weather forecasting from comparatively easy observation of the sky to extremely advance computerized mathematical Models [6].…”

Section: Introductionmentioning

confidence: 99%

A Study of Time Series Models ARIMA and ETS

Jain¹,

Mallick²

2017

IJMECS

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The aim of the study is to introduce some appropriate approaches which might help in improving the efficiency of weather's parameters. Weather is a natural phenomenon for which forecasting is a great challenge today. Weather parameters such as Rainfall, Relative Humidity , Wind Speed , Air Temperature are highly non-linear and complex phenomena, which include statistical simulation and modeling for its correct forecasting. Weather Forecasting is used to simplify the purpose of knowledge and tools which are used for the state of atmosphere at a given place. The expectations are becoming more complicated due to changing weather state. There are different software and their types are available for Time Series forecasting. Our aim is to analyze the parameter and do the comparison of some strategies in predicting these temperatures. Here we tend to analyze the data of given parameters and to notice their predictions for a particular period by using the strategy of Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) .The data from meteorological centers has been taken for the comparison of methods using packages such as ggplot2, forecast, time Date in R and automatic prediction strategies which are available within the package applied for modeling with ARIMA and ETS methods. On the basis of accuracy we tend to attempt the simplest methodology and then we will compare our model on the basis of MAE, MASE, MAPE and RMSE. An identification of model will be the chromatic checkup of both the ACF and PACF to hypothesize many probable models which are going to be projected by selection criteria i.e. AIC, AICc and BIC.

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An Integrated Approach for Weather Forecasting based on Data Mining and Forecasting Analysis

Cited by 15 publications

References 13 publications

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

Smart Energy Harvesting and Severe Weather Alert System

A Study of Time Series Models ARIMA and ETS

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