A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(34), С. 82964 - 82989

Опубликована: Июнь 19, 2023

Язык: Английский

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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2024, Номер 18(1)

Опубликована: Янв. 7, 2024

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for training dataset, 80% dataset from sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used create an susceptibility model establishes unique original approach. After it confirmed be successful, algorithms were applied map this area, demonstrating integrity results. The AUC values computed every optimisation in study. optimal estimated accuracy indices populations 450 determined 0.9846 BSA-MLP databases. maximum value HBO-MLP databases different swarm sizes 0.9736. A size 350–300 is considered forecasting mapping models. With same constraints, TLBO-MLP scenario 0.996. 150 conditions train PO-MLP model, 0.9845. According these findings, worked best 50 150, respectively.

Язык: Английский

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Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas

Landslides, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

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Development of a new hybrid model to enhance streamflow estimation using artificial neural network and reptile search algorithm

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 19, 2025

Abstract A new metaheuristic optimizer combined with artificial neural networks is proposed for streamflow prediction. Hence, the study aimed to forecast monthly of main rivers in Urmia, Iran, by considering data shortage and using network (ANN) models. By combining three variables: temperature, precipitation, streamflow, we formulated five patterns, where 70% were used model training, 30% testing. To improve performance ANN, evaluated a optimization algorithm, reptile search algorithm (RSA), compared results combinations particle swarm (PSO), whale (WOA) The ANN + RSA promising at most stations patterns. At Band station simulation testing gave RMSE, MAE, NSE 1.65, 1.21 MCM/month, 0.80, respectively. Babaroud they 4.01, 3.0 MCM/month 0.68, respectively, Nazlo 5.62, 3.79 0.69, Tapik 5.69, 3.82 0.59, However, PSO hybrid better than RSA. impact different parameters on accuracy prediction varied depending station, indicating that models do not perform consistently across locations, times, conditions. inclusion lagged was an influential input parameter. demonstrated improved predictions, enhancing traditional algorithms. findings this highlight advantage specific areas, suggesting its potential application other similar hydrological problems further validation.

Язык: Английский

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A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер unknown

Опубликована: Март 21, 2024

Язык: Английский

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Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed

Water Resources Management, Год журнала: 2023, Номер 38(2), С. 553 - 567

Опубликована: Дек. 5, 2023

Язык: Английский

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Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 134, С. 103563 - 103563

Опубликована: Янв. 28, 2024

Язык: Английский

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Landslide susceptibility assessment using novel hybridized methods based on the support vector regression

Ecological Engineering, Год журнала: 2024, Номер 208, С. 107372 - 107372

Опубликована: Авг. 20, 2024

Язык: Английский

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Application of Naive Bayes, kernel logistic regression and alternation decision tree for landslide susceptibility mapping in Pengyang County, China

Natural Hazards, Год журнала: 2024, Номер 120(13), С. 12043 - 12079

Опубликована: Май 25, 2024

Язык: Английский

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A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(59), С. 123527 - 123555

Опубликована: Ноя. 21, 2023

Язык: Английский

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