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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Vydáno v:International journal of system assurance engineering and management Ročník 15; číslo 7; s. 3182 - 3192
Hlavní autoři: Lathika, P., Singh, D. Sheeba
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.07.2024
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-024-02324-9