Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

Shaiba, Hadil; Marzouk, Radwa; Nour, Mohamed K.; Negm, Noha; Hilal, Anwer Mustafa; Mohamed, Abdullah; Motwakel, Abdelwahed; Yaseen, Ishfaq; Zamani, Abu Sarwar; Rizwanullah, Mohammed

doi:10.32604/cmc.2022.030067

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…The SqueezeNet model, based on deep convolutional neural networks, is used by the reported MVODTL-FD technique to extract features. While requiring fifty times as many variables as AlexNet, SqueezeNet is a convolutional network that performs better derived by Shaiba H. (2022). Among the 15 layers that make up SqueezeNet are one global average pooling layer, one softmax output layer, two convolution layers, three maxpooling levels, and eight fire layers.…”

Section: Feature Extractionmentioning

confidence: 99%

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

2023

Global NEST Journal

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<p>Floods are one of the leading causes of damage, prompting mortality and substantial destruction to the structure and total economy of the affected nations. Remote sensing, satellite imagery, global positioning system, and geographic information system (GIS) are widely employed for flood identification to examine flood-related losses. Recently, accurate and automated flood detection models using remote sensing images have become effective for flood disaster management, risk manager, infrastructure planning, disaster rescue management, etc. Computer vision and deep learning (DL) models provide prompt and rapid flood detection in remote sensing images. In this aspect, this paper presents a multiverse optimization with a deep transfer learning-enabled flood detection (MVODTL-FD) technique for disaster risk management. In the proposed MVODTL-FD technique, remote sensing images are investigated for the effectual detection of floods. To accomplish this, the presented MVODTL-FD technique applies a guided normal filter (GNF) based image preprocessing approach to eliminate the noise. In addition, the proposed MVODTL-FD technique uses a deep convolutional neural network-based Squeeze Net model for feature extraction, and the hyperparameter process is performed using the MVO algorithm. At last, the flood detection process is performed using support vector machine (SVM) classification. For establishing the improved version of the MVODTL-FD method, a wide-ranging experimental analysis is performed. The MVODTL-FD model is rated higher in the comparative analysis than other DL models.</p>

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Section: Feature Extractionmentioning

confidence: 99%

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

2023

Global NEST Journal

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“…Therefore, rainfall trend detection and future prediction remain an important field of study for Bangladesh. The weather, including temperature, wind direction, speed, and amount of rainfall, can be predicted using machine learning algorithms [18]. This research work uses machine learning algorithms and implements ensemble-based regression and classifier models using the Bangladesh weather dataset to perform the weather predictions, including rainfall occurrence, rainfall amount, and daily average temperature prediction.…”

Section: Introductionmentioning

confidence: 99%

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Hussain,

Tripura,

Aslam

2024

Preprint

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Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

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Multivariate Time Series Forecasting based on Time-Frequency Decomposition

Li,

Hao,

Wang

et al. 2023

2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS)

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Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

Cited by 7 publications

References 8 publications

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

Integrated Disaster Risk Management for Flood Detection on Remote Sensing Images using Deep Learning techniques

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Multivariate Time Series Forecasting based on Time-Frequency Decomposition

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