Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) comb...

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Bibliographic Details
Published inAtmosphere Vol. 13; no. 9; p. 1436
Main Authors Alawsi, Mustafa A., Zubaidi, Salah L., Al-Ansari, Nadhir, Al-Bugharbee, Hussein, Ridha, Hussein Mohammed
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Accuracy
Algorithms
Artificial intelligence
artificial neural network
Artificial neural networks
Climate change
Climate change influences
Climatic indexes
Drought
drought forecast model
Drought forecasting
Droughts
Forecasting
Friction stir welding
Geoteknik
Gravity
Hydrology
Iraq
Management
Mathematical models
metaheuristic algorithms
Methods
Neural networks
Particle swarm optimization
Precipitation
Predators
Preprocessing
Rain
Search algorithms
Slime molds
Soil Mechanics
Spectrum analysis
standardised precipitation index
Time series
Water
Water management
Water resources
Water resources management
Water shortages
Weather forecasting
Iraq
Arabian Peninsula
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Summary:Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos13091436