Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration DOI
Sani I. Abba, Rabiu Aliyu Abdulkadir,

Saad Sh. Sammen

и другие.

Hydrological Sciences Journal, Год журнала: 2021, Номер 66(10), С. 1584 - 1596

Опубликована: Июнь 3, 2021

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. network (EANN), feedforward (FFNN), (NNE), to predict DO in Kinta River basin Malaysia. The performance EANN-GA, EANN, FFNN, NNE models predicting was evaluated using statistical metrics visual interpretation. Appraisal results revealed a promising NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) 0.9351/0.9113, mean square error (MSE) 0.5757/0.6833 mg/L, root (RMSE) 0.7588/0.8266 absolute percentage (MAPE) 20.6581/14.1675) during calibration/validation period compared FFNN basin.

Язык: Английский

Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM DOI
Wencheng Huang, Hongyi Liu, Yue Zhang

и другие.

Applied Soft Computing, Год журнала: 2021, Номер 109, С. 107541 - 107541

Опубликована: Май 31, 2021

Язык: Английский

Процитировано

127

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study DOI Creative Commons
Junwei Ma, Ding Xia, Haixiang Guo

и другие.

Landslides, Год журнала: 2022, Номер 19(10), С. 2489 - 2511

Опубликована: Июнь 30, 2022

Abstract Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) particle swarm (PSO) and water cycle (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), support vector regression (SVR), nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, convergence. results obtained Shuping Baishuihe landslides demonstrate that can be utilized determine optimum hyperparameters present statistical significance, thus enhancing accuracy reliability ML-based Significant differences observed among metaheuristics. Based on test, which was performed root mean square error (RMSE), Kling-Gupta efficiency (KGE), PSO recommended hyperparameter tuning SVR-based prediction due its ability maintain balance between precision, robustness. promising presenting

Язык: Английский

Процитировано

94

Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes DOI Creative Commons
Pravin Bhasme,

Jenil Vagadiya,

Udit Bhatia

и другие.

Journal of Hydrology, Год журнала: 2022, Номер 615, С. 128618 - 128618

Опубликована: Ноя. 8, 2022

Язык: Английский

Процитировано

88

Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran DOI
Mohammad Akbarian, Bahram Saghafian, Saeed Golian

и другие.

Journal of Hydrology, Год журнала: 2023, Номер 620, С. 129480 - 129480

Опубликована: Апрель 12, 2023

Язык: Английский

Процитировано

74

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions DOI
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 129, С. 107559 - 107559

Опубликована: Дек. 3, 2023

Язык: Английский

Процитировано

64

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm DOI Creative Commons
Babak Mohammadi

Hydrology, Год журнала: 2023, Номер 10(3), С. 58 - 58

Опубликована: Фев. 27, 2023

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, standardized precipitation index (SPI) was monitored predicted Peru between 1990 2015. The study proposed a hybrid model, called ANN-FA, for SPI time scales (SPI3, SPI6, SPI18, SPI24). A state-of-the-art firefly algorithm (FA) has been documented as powerful tool to support modeling issues. ANN-FA uses an artificial neural network (ANN) which is coupled with FA Lima via other stations. Through intelligent utilization series from neighbors’ stations model inputs, suggested approach might be used forecast at meteorological station insufficient data. To conduct this, SPI3, SPI24 were modeled using stations’ datasets Peru. Various error criteria employed investigate performance model. Results showed that effective promising drought also multi-station strategy lack results can help predict mean absolute = 0.22, root square 0.29, Pearson correlation coefficient 0.94, agreement 0.97 testing phase best estimation (SPI3).

Язык: Английский

Процитировано

51

Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization DOI
Yuansheng Gao, Jiahui Zhang, Yulin Wang

и другие.

The Journal of Supercomputing, Год журнала: 2024, Номер 80(9), С. 12346 - 12407

Опубликована: Фев. 12, 2024

Язык: Английский

Процитировано

23

Advances in Spotted Hyena Optimizer: A Comprehensive Survey DOI

Shafih Ghafori,

Farhad Soleimanian Gharehchopogh

Archives of Computational Methods in Engineering, Год журнала: 2021, Номер 29(3), С. 1569 - 1590

Опубликована: Июль 5, 2021

Язык: Английский

Процитировано

93

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India DOI

Kusum Pandey,

Shiv Kumar,

Anurag Malik

и другие.

Sustainability, Год журнала: 2020, Номер 12(21), С. 8932 - 8932

Опубликована: Окт. 27, 2020

Accurate information about groundwater level prediction is crucial for effective planning and management of resources. In the present study, Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed seasonal table depth (GWTD) in area between Ganga Hindon rivers located Uttar Pradesh State, India. A total 18 models both seasons (nine pre-monsoon nine post-monsoon) have been formulated by using recharge (GWR), discharge (GWD), previous data from 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability evaluated against traditional GA based on statistical indicators visual inspection. results appraisal indicates that outperformed predicting GWTD study region. Overall, GA-ANN-8 model an 8-9-1 structure (i.e., 8: inputs, 9: neurons hidden layer, 1: output) nominated optimal during pre- post-monsoon seasons. Additionally, it noted maximum number input variables approach improved accuracy. conclusion, proposed model’s findings could be readily transferable or implemented other parts world, specifically those similar geology hydrogeology conditions sustainable resources management.

Язык: Английский

Процитировано

85

Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms DOI
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez

и другие.

Applied Energy, Год журнала: 2022, Номер 316, С. 119063 - 119063

Опубликована: Апрель 19, 2022

Язык: Английский

Процитировано

71