Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin

Zerouali, Bilel; Santos, Celso Augusto Guimarães; Farias, Camilo Allyson Simões de; Muniz, Raul Souza; Heddam, Salim; Abda, Zaki; Chettih, Mohamed; Heddam, Salim; Anwar, Samy A.; Elbeltagi, Ahmed

doi:10.1016/j.heliyon.2023.e15355

Cited by 11 publications

(2 citation statements)

References 76 publications

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“…We used multiple performance metrics to assess the model's effectiveness, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and the Pearson Correlation Coefficient (R). Concise explanations of each performance measure are presented below [65,66].…”

Section: Performance Criteriamentioning

confidence: 99%

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Hussein,

Derdour,

Zerouali

et al. 2024

Water

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The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

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Section: Performance Criteriamentioning

confidence: 99%

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Hussein,

Derdour,

Zerouali

et al. 2024

Water

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“…PSO does this by moving particles within a search space using simple mathematical formulas based on their positions and velocities. The movement of each particle is affected by its locally most-favored position and directed towards superior positions in the search area [ 43 , 44 ], which get updated as improved positions are discovered.…”

Section: Multilayer Perceptron Optimized By Particle Swarm Optimizati...mentioning

confidence: 99%

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

Gomaa¹,

Zerouali²,

Difi³

et al. 2023

Heliyon

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Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices

Ghanbarzadeh,

Ghorbani,

Salarijazi

et al. 2024

Atmospheric Science Letters

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In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year.

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Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin

Cited by 11 publications

References 76 publications

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices

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