Hybrid dynamic arithmetic city council optimization for improved rainfall prediction

In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To pred...

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Veröffentlicht in:International journal of system assurance engineering and management Jg. 15; H. 7; S. 3182 - 3192
Hauptverfasser: Lathika, P., Singh, D. Sheeba
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.07.2024
Springer Nature B.V
Schlagworte:
Accuracy
Agriculture
Algorithms
Arithmetic
Artificial intelligence
Correlation analysis
Datasets
Deep learning
Developing countries
Effectiveness
Efficiency
Engineering
Engineering Economics
Evolutionary algorithms
Forecasting
LDCs
Logistics
Marketing
Missing data
Municipal government
Neural networks
Optimization
Organization
Original Article
Precipitation
Preprocessing
Quality Control
Rain
Rainfall
Reliability
Root-mean-square errors
Safety and Risk
Task complexity
Wavelet transforms
India
City councils evolution algorithm
Open weather dataset
Dynamic arithmetic optimization algorithm
Correlation matrix
Rainfall prediction
ISSN:0975-6809, 0976-4348
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Abstract In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid Dynamic Arithmetic City Council Optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the Dynamic Arithmetic Optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R 2 ), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R 2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction.
AbstractList In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid Dynamic Arithmetic City Council Optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the Dynamic Arithmetic Optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R2), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction.
In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid Dynamic Arithmetic City Council Optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the Dynamic Arithmetic Optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R 2 ), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R 2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction.
Author Lathika, P.
Singh, D. Sheeba
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The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.
Copyright_xml – notice: The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Issue 7
Keywords City councils evolution algorithm
Open weather dataset
Dynamic arithmetic optimization algorithm
Correlation matrix
Rainfall prediction
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Snippet In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a...
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SubjectTerms Accuracy
Agriculture
Algorithms
Arithmetic
Artificial intelligence
Correlation analysis
Datasets
Deep learning
Developing countries
Effectiveness
Efficiency
Engineering
Engineering Economics
Evolutionary algorithms
Forecasting
LDCs
Logistics
Marketing
Missing data
Municipal government
Neural networks
Optimization
Organization
Original Article
Precipitation
Preprocessing
Quality Control
Rain
Rainfall
Reliability
Root-mean-square errors
Safety and Risk
Task complexity
Wavelet transforms
Title Hybrid dynamic arithmetic city council optimization for improved rainfall prediction
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Volume 15
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