Grey Wolf-Based Linear Regression Model for Rainfall Prediction

Mohd, Razeef; Butt, Muheet Ahmed; Baba, Majid Zaman

doi:10.4018/ijitsa.290004

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Second, by using the MSE as an evaluation metric, the MSE of GWO is 20.54, and the MSE for Firefly, GA, PSO, ZOA, PDO, WSO, and MFO models are 47. 74 Sixth, the R 2 as an evaluation metric is used to measure the extent of the relationship between the independent variable and the dependent variable. It can be observed that GWO has the highest value among all other models.…”

Section: B Experimental Results For the Cocomo Model I On The Nasa18 ...mentioning

confidence: 99%

“…• Attacking the victim. The first reason is that the GWO has proven its efficiency in studies similar to the nature of our research problem, such as in [74], where they proposed a model to predict rainfall based on the input time-series weather data using GWO in India, Jammu, and Kashmir. They used 4 datasets.…”

Section: The Meta-heuristic Algorithms a Grey Wolf Optimization Algor...mentioning

confidence: 99%

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Improving Software Effort Estimation Models Using Grey Wolf Optimization Algorithm

Alsheikh,

Munassar

2023

IEEE Access

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One of the phases in the Software Development Life Cycle is planning the software project. Estimating the software effort is another task in this project planning phase. Software effort estimation is the method of determining how many workers are required to create a software project. To increase the precision of software effort estimation, many researchers have focused on this field and used both algorithmic and non-algorithmic techniques. The most widely used method is the Constructive Cost Model (COCOMO). However, the COCOMO model has a limitation, which is related to the precision of the software effort estimation. Meta-heuristic algorithms are preferred with parameter optimization because they can provide solutions that are nearly optimal at a reasonable cost. This study aims to enhance the precision of effort estimation by modifying the three COCOMO-based models' coefficients and assess the efficiency of Grey Wolf Optimization (GWO) in finding the optimal value of effort estimation through applying four other algorithms, including Zebra Optimization(ZOA), Moth-Flame Optimization(MFO), Prairie Dog Optimization(PDO), and White Shark Optimization(WSO) with NASA18 dataset. These models include the basic COCOMO model, and another two models were also suggested in the published research as a modification of the basic COCOMO model. To assess the performance of the proposed models, the six most used software effort estimation metrics are used. The results demonstrated that the GWO outperformed other algorithms based on high accuracy and significant error minimization, involving ZOA, MFO, PDO, WSO, and other existing models.

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Section: B Experimental Results For the Cocomo Model I On The Nasa18 ...mentioning

confidence: 99%

Section: The Meta-heuristic Algorithms a Grey Wolf Optimization Algor...mentioning

confidence: 99%

Improving Software Effort Estimation Models Using Grey Wolf Optimization Algorithm

Alsheikh,

Munassar

2023

IEEE Access

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“…The correlation is reflected in the fact that precipitation acts as a common source of recharge for both surface water and groundwater, while the source of recharge for precipitation comes from evaporation, the same discharge method for both surface water and groundwater, thus bringing the 'three waters' into the same cyclic relationship. The constraint system is that there are checks and balances on the amount and timing of transformations between the three waters [15][16].…”

Section: The "Three Waters" Conversion Relationship In a River Basinmentioning

confidence: 99%

Linear Regression for Water Pollution Control Planning under Support Vectors

2021

WPPCP

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China is facing a serious situation in water pollution prevention and control, and the uncertainty of regional development should be fully considered when formulating water pollution prevention and control planning, and its scientificity should be improved through effective mathematical methods. Water resources and water environment are closely related to human health, and China has not yet established a comprehensive set of water resources and environment monitoring network, so the water pollution prevention and control planning process is crucial to the implementation of water pollution control planning. Due to the relatively low level of economic development in China, the state attaches less importance to water pollution prevention and control issues, and therefore also has a certain impact on the water environment. In order to reduce the problems caused by water pollution, the Water Resources Management Committee has organised the preparation of water pollution control plans. This paper presents an empirical analysis of the support vector model for water pollution prevention and control planning in China. The results of the study show that the introduction of the support vector approach to water pollution prevention and control planning in China can effectively improve the scientific nature of the planning. This paper firstly introduces the support vector model, which is a multivariate model designed based on the support vector method, and can be used to analyse multiple indicators and conduct comprehensive analysis in the prediction process; finally, the planning scheme should be designed with full consideration of water pollution prevention and control planning to avoid conflicts between different water resources and environmental issues. However, there are many problems with the use of the support vector approach in water pollution prevention and control planning that warrant further research.

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“…Artificial Intelligence (AI) is rapidly transforming our world. With time it has spread into many business areas [1][2][3] and is originating in medical field too with the increase in the complication and evolution of data in biological sciences, AI is gradually being applied within the field. The AI technologies such as machine learning (ML) and deep learning (DL) [4][5][6][7] have played a foremost role in the detection and prediction of diseases [8,9] either by means of the disease symptom datasets [10,11] or medical image datasets, which have helped the doctors in a positive way.…”

Section: Introductionmentioning

confidence: 99%

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

Altaf

Butt²,

Zaman³

2022

Self Cite

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To predict and detect various diseases, machine learning techniques are increasingly being used in the field of medical science. This study puts forward a bagging meta-estimator and feed forward neural network based voting ensemble with mean decrease in impurity feature selection to classify the disease datasets. The work was carried out using the Jupyter notebook data analysis tool, and Python 3 is used as a programming language. In this study, two chronic disease datasets - Indian Liver Patient dataset and the PIMA Indians diabetes dataset are used for building and testing the proposed model. The datasets are split into training and testing data in the ratio of 70:30. The experimental results illustrate that our proposed voting ensemble has an improved performance compared to the individual base learners. We also compared the accuracy of the model before and after the application of feature reduction technique. The results revealed that the accuracy increased with the removal of unimportant features. By using the proposed ensemble model, the average MSE, bias and variance were calculated as 0.311, 0.217 and 0.094 respectively for ILPD dataset. Similarly for PIMA dataset, the average MSE, bias and variance were calculated as 0.233, 0.186 and 0.047 respectively. These statistical parameters record a low score for ensemble classifier as compared to the individual constituent classifiers.

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Grey Wolf-Based Linear Regression Model for Rainfall Prediction

Cited by 7 publications

References 22 publications

Improving Software Effort Estimation Models Using Grey Wolf Optimization Algorithm

Improving Software Effort Estimation Models Using Grey Wolf Optimization Algorithm

Linear Regression for Water Pollution Control Planning under Support Vectors

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

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