Weather Forecasting using Soft Computing Techniques

Bhardwaj, Rashmi; Duhoon, Varsha

doi:10.1109/gucon.2018.8675088

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…A model is implemented that has a nature to augment algorithmic rule that gives approximate or nearby results for forecasting of upcoming five days and at the last outcomes is calculated with a crisp and statistical decision tree and prepare a confusion matrix for forecasting using Big Data [8]. The Authors have predicted rainfall and temperature using four different algorithms based on different error parameters and concluded that among several machine learning algorithms, for rainfall prediction multi-Layer perceptron (MLP) is the best and for temperature prediction best model is Support vector Regression [9]. The Authors reviewed IoT data features and their difficulties with Deep Learning (DL) methods [10].…”

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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“…Different soft computing techniques to forecast weather parameters have been studied in [4]. In [5], it has been demonstrated that, on integrating ANFIS (Adaptive Neuro-Fuzzy Inference System) with Sugeno model and applying the resultant ANFIS-Sugeno model on weather time-series data like daily temperature, the error in the output gets reduced.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Models for Weather Parameter Forecasting

Bhardwaj

Duhoon

2021

Complexity

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The objective of the paper is to compare hybrid conjunction models with conventional models for the reduction of errors in weather forecasting. Besides the simple models like RBF model, SMO model, and LibSVM model, different hybrid conjunction models have been used for forecasting under different schemes. The forecasts from these models are further compared on the basis of errors calculated and time taken by the hybrid models and simple models in order to forecast weather parameters. In this paper, conjunction models over the convectional models are designed for forecasting the weather parameters for the reduction of error. India is a tropical country with variations in weather conditions. The objective is to build a conjunction model with less error to forecast weather parameters. A hybrid conjunction model is developed and analysed for different weather parameters for different metropolitan cities of India. Performance measurement is analysed for weather parameters. It is observed that, on the basis of error comparison and time taken by the models, the hybrid wavelet-neuro-RBF model gives better results as compared to the other models due to lower values of determined errors, better performance, and lesser time taken. The study becomes significant as weather forecasting with accuracy is a complex task along with the reduction of prediction error by the application of different models and schemes. It is concluded that the proposed hybrid model is helpful for forecasting and making policies in advance for the betterment of the human being, farmers, tourists, and so on as in all these activities, weather forecast plays an important role.

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