A Weather Forecasting Model using the Data Mining Technique

Yadav, Rohit; Khatri, Ravi

doi:10.5120/ijca2016908900

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…Still, the drawback is this it is only one parameter that can't be used for exact prediction as weather includes many permanents. In the paper [4], a data mining algorithm for weather forecasting was implemented. The model used for prediction is a Hidden Markov Model (HMM), and the K-means clustering algorithm extracts appropriate data about weather conditions.…”

Section: Related Workmentioning

confidence: 99%

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Yadav¹,

Singh²,

Tiwari³

2023

Computer Systems Science and Engineering

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Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate. However, effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology. The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage. Currently, most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere. In this work, sensors are used to collect real-time data for a particular location to capture the varying nature of the atmosphere. Our solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of Things. The Authors identified weatherrelated parameters such as temperature, humidity, wind speed, and rainfall and then applied cognitive data collection methods to train and validate their findings. In addition, the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e., pre-recorded metrological data sets and live sensor data sets collected from multiple locations. The Authors noticed that the results were superior on the sensor data. The Authors developed ensemble learning model using stacked method that achieved 99.25% accuracy, 99% recall, 99% precision, and 99% F1-score for Sensor data. It also achieved 85% accuracy, 86% recall, 85% precision, and 86% F1 score for Australian rainfall data.

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Section: Related Workmentioning

confidence: 99%

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Yadav¹,

Singh²,

Tiwari³

2023

Computer Systems Science and Engineering

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“…This is on the grounds that straightforward perceptron's are appropriate for displaying direct connections, which is the reason the incorporation of concealed layers denotes an achievement in ANN advancement. There exist a wide range of types of MLPs in the writing, yet most can be said to differ as indicated by five principal criteria: (I) course of data stream (input or feedforward), (ii) preparing technique, (iii) learning calculation, (iv) number of shrouded layers, and (v) mistake work (Lutgens et al, 1995;Kumar and Rohit, 2016).…”

Section: Hybrid Markov Modelmentioning

confidence: 99%

Joint Weather Predicting by HMM and Ann

Degadwala¹,

Vyas²,

Mahajan³

2020

Ethics and Information Technology

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In India, climate change tremendously due to the flow of wind, temperature, precipitation, etc. based on parameters. Our idea is to combine the two city or states data to calculate dependence on the neighboring city/states. Also, to analysis past history data of climate 21 years. To predicate the future five years. To show, that the it will predict the future five years of data. By mutual interaction of the most predication models Hybrid Markova model and artificial neural network. ANN and HMM will both predicate the flow two cities and states due to which the predication analysis of climate India will be accurate. This paper defines the data analysis on predication based model and due to which the flow of model will be accurate. This paper shows a strategy to conjecture and settles on a choice of weather condition. In the majority of the urban communities around the globe, individuals attempt to settle on relaxation exercises on their extra time however climate condition would not be appropriate for them. Since the climate conditions in India are flighty, a methodology must be created to gauge climate effectively. By gauging climate definitely, we can anticipate and beat numerous dangers that could prompt extraordinary misfortune to a country. Thus, so as to do this, the Hidden Markov Model and Artificial Neural Network has been deciding for shared cooperation of two states to foresee the climate condition and as yield third states are been predicated.

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“…Accurate temperature forecasts enable decision-makers, businesses, and individuals to plan effectively, mitigate risks, and optimise resource allocation. Over the years, advancements in computational techniques have transformed weather forecasting from a traditional, data-driven endeavour into a data-intensive scientific discipline (Kumar, 2023). The quality of weather forecasting is fundamentally dependent on the data on which the forecasting model is based.…”

Section: Introductionmentioning

confidence: 99%

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

Ayodele,

Adekola,

Ogunseye

et al. 2024

fuoyejet

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Accuracy and model compactness are essential requirements for weather forecasting models designed for operation on low-power embedded devices. This study developed Mixed-Input Residual Network (MIRNet), a compact temperature-forecasting deep neural network model. MIRNet integrates stacked bidirectional long short-term memory layers using concatenated 1-dimensional and 2-dimensional convolutional layers to improve model accuracy. MIRNet was trained and tested on two datasets: one, IfeData, comprising historical weather data from Ile-Ife, Nigeria and the other a standard weather forecasting dataset called the Jena dataset. Training was carried out using 100 epochs of data partitioned in the standard 80:20 ratio, with an adaptable learning rate strategy. The model was tested for Nth-hour-ahead prediction for 1N24; where N are natural numbers, and performance quantified using metrics such as mean absolute percentage error (MAPE) and mean square error (MSE). The model was also implemented on a Raspberry Pi 4 device with a 1.8 GHz 64-bit quad-core ARM Cortex-A72 processor. The model achieved a MSE of 1.00 x 10-3 on the IfeData dataset, and 1.23x10-4 on the Jena dataset for 1-hour ahead forecasting. This is currently the best verifiable result achieved on the Jena dataset by any prediction model globally. For Nth hour ahead forecasting, MIRNet achieved an MSE generally below 2.0x10-3 for all values of N on the standard Jena dataset. The MSE of MIRNet for N-sequential 1-hour ahead and single Nth hour predictions using the Jena dataset reveal quadratic and linear relationships with N respectively. The model compares favourably with existing models for multi-hour predictions. The developed model is compact and has good forecasting properties.

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A Weather Forecasting Model using the Data Mining Technique

Cited by 15 publications

References 16 publications

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Joint Weather Predicting by HMM and Ann

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

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