Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Shad, Mohammad; Sharma, Y. D.; Singh, Abhishek

doi:10.1007/s40808-022-01385-8

Cited by 17 publications

(13 citation statements)

References 44 publications

(37 reference statements)

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“…Further, for the testing results, R 2 and R for ANN were 94% and 97%, while the results for MLR were 97% and 98% accuracy. We can present and organize the findings from our predictive comparison in the following way: Regarding the prediction of COVID-19, MLR was superior to ANN, and this result is similar to the findings of [ 6 , 7 , 23 , 24 , 33 , 34 ]. Additionally, ref.…”

Section: Application Of Results and Discussionsupporting

confidence: 72%

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COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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Section: Application Of Results and Discussionsupporting

confidence: 72%

“…With some tweaks to the weights and biases of the connections between neurons, an artificial neural network can learn to perform a wide variety of tasks. Image recognition, text translation, and stock market forecasting are just some of the many tasks that can be taught to a neural network [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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“…This is because radar products, such as radar reflectivity, can help them learn more about the stage and development of thunderstorms, such as how they start, grow vertically, and end. However, [6] in addition, two lightning wildfires were observed, with one occurring in the eucalyptus forest near Melbourne, followed by a rapid forest fire spread over the next two days. During the Australian Black Summer in the southeastern Australian province of Victoria, no lightning-caused forest fires were documented.…”

Section: Discussionmentioning

confidence: 99%

“…To have an average prediction of thunderstorms, they have to collect and analyze vast Observational data from tens of thousands of weather stations. When these methods are used, the time and accuracy have improved [6].…”

Section: Machine Learningmentioning

confidence: 99%

Thunderstorms Prediction Using Satellite Images

Amjad¹,

Malik²,

Ghous³

et al. 2023

ijisct

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Human life and property are put at risk by lightning, heavy hail, and strong winds caused by thunderstorms. Accurate prediction of thunderstorms has long been a difficult problem. In difference from conventional methods, the modern methods for predicting thunderstorms utilize a combination of computer models, observation (via the use of radars and satellite photos), and modern knowledge and patterns employed by thunderstorms stations. Observations: Using these methods, it is possible to make reasonably accurate predictions. Prediction models based on complex formulas run the majority of forecasting computer models. Artificial neural networks, machine learning, deep learning, and statistics are all examples of adaptive systems that alter their structure in response to information flowing through the network. In this reviewed paper, different Artificial intelligence techniques are used such as machine learning, deep learning, and ANN (Artificial neural network). Different statistical models were presented to predict thunderstorm systems. Various researchers achieved technical milestones by presenting statistical methods on thunderstorm systems using satellite data.

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“…Shad et al (2022) explored seasonal autoregressive moving averages (SARIMA) and ANN with multilayer perceptron models for forecasting monthly relative humidity in Delhi, India, between 2017 and 2025. This study tried to use SARIMA and ANN with MLP models.…”

Section: Previous Workmentioning

confidence: 99%

Deep learning model for temperature prediction: A case study in New Delhi

Shrivastava

Sharma

et al. 2023

Journal of Forecasting

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This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence.

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Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Cited by 17 publications

References 44 publications

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Thunderstorms Prediction Using Satellite Images

Deep learning model for temperature prediction: A case study in New Delhi

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