Modeling of Fuzzy Cognitive Maps with a Metaheuristics-Based Rainfall Prediction System

Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rai...

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Bibliographic Details
Published in:Sustainability 2023-01, Vol.15 (1), p.25
Main Authors: Al Duhayyim, Mesfer, Mohamed, Heba G., Alzahrani, Jaber S., Alabdan, Rana, Mousa, Mohamed, Zamani, Abu Sarwar, Yaseen, Ishfaq, Alsaid, Mohamed Ibrahim
Format: Article
Language:English
Subjects:
Artificial intelligence
Climate change
Cognitive maps
Cognitive models
Datasets
Decomposition
Flight operations
Forecasting
Hydrologic data
Neural networks
Optimization
Parameter modification
Performance assessment
Predictions
Process parameters
Rain
Rainfall
Security
Smart cities
Sustainability
Task complexity
Time series
Urban environments
Weather forecasting
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Summary:Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than before because of extreme climatic changes. Machine learning (ML) approaches can forecast rainfall by deriving hidden patterns from historic meteorological datasets. Selecting a suitable classification method for forecasting has become a tough job. This article introduces the Fuzzy Cognitive Maps with a Metaheuristics-based Rainfall Prediction System (FCMM-RPS) technique. The intention of the FCMM-RPS technique is to predict rainfall automatically and efficiently. To accomplish this, the presented FCMM-RPS technique primarily pre-processes the rainfall data to make it compatible. In addition, the presented FCMM-RPS technique predicts rainfall using the FCM model. To enhance the rainfall prediction outcomes of the FCM model, the parameter optimization process is performed using a modified butterfly optimization algorithm (MBOA). The performance assessment of the FCMM-RPS technique is tested on a rainfall dataset. A widespread comparison study highlights the improvements of the FCMM-RPS technique in the rainfall forecasting process compared to existing techniques with a maximum accuracy of 94.22%.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15010025