Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 5, 2024

Abstract Prediction of suspended sediment load (SSL) in streams is significant hydrological modeling and water resources engineering. Development a consistent accurate prediction model highly necessary due to its difficulty complexity practice because transportation vastly non-linear governed by several variables like rainfall, strength flow, supply. Artificial intelligence (AI) approaches have become prevalent resource engineering solve multifaceted problems modelling. The present work proposes robust incorporating support vector machine with novel sparrow search algorithm (SVM-SSA) compute SSL Tilga, Jenapur, Jaraikela Gomlai stations Brahmani river basin, Odisha State, India. Five different scenarios are considered for development. Performance assessment developed analyzed on basis mean absolute error (MAE), root squared (RMSE), determination coefficient (R 2 ), Nash–Sutcliffe efficiency (E NS ). outcomes SVM-SSA compared three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper SVM-BA (Bat benchmark SVM model. findings revealed that successfully estimates high accuracy scenario V (3-month lag) discharge (current time-step 3-month as input than other alternatives RMSE = 15.5287, MAE 15.3926, E 0.96481. conventional performed the worst prediction. Findings this investigation tend claim suitability employed approach rivers precisely reliably. guarantees precision forecasted while significantly decreasing computing time expenditure, satisfies demands realistic applications.

Language: Английский

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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Environmental Pollution, Journal Year: 2024, Volume and Issue: 351, P. 124040 - 124040

Published: April 27, 2024

Language: Английский

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Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 1, 2024

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In current research, EC two Australian rivers (Albert River Barratta Creek) was forecasted up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method used determine significant inputs (time series lagged data) model. To compare performance Boruta-XGB-CNN-LSTM models, three machine approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), extreme gradient boosting (XGBoost) were used. Different statistical metrics, such correlation coefficient (R), root mean square error (RMSE), absolute percentage error, assess models' performance. From years data in both rivers, 7 (2012-2018) training set, 3 (2019-2021) testing models. Application model forecasting day ahead showed that stations, can forecast parameter better than other models test dataset (R = 0.9429, RMSE 45.6896, MAPE 5.9749 Albert River, R 0.9215, 43.8315, 7.6029 Creek). Considering this 3-10 EC. results very capable next days. by increasing horizon from days, slightly decreased. study show be good soft computing accurately how will change rivers.

Language: Английский

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PM2.5 concentration forecasting: Development of integrated multivariate variational mode decomposition with kernel Ridge regression and weighted mean of vectors optimization

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(6), P. 102125 - 102125

Published: March 20, 2024

Language: Английский

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Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(22), P. 32382 - 32406

Published: April 23, 2024

Language: Английский

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Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131704 - 131704

Published: July 20, 2024

Language: Английский

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Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124900 - 124900

Published: July 30, 2024

Language: Английский

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Monthly rainfall prediction model based on VMD-PSO-BiLSTM-case study: Handan City, China

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(2)

Published: Feb. 1, 2025

Language: Английский

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Advanced deep learning approaches for superior temporal analysis and forecasting of water level discharge for the Bamni River

International Journal of River Basin Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 12, 2025

Language: Английский

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Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model

Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 14, 2025

ABSTRACT Streamflow ( Q flow ) process is one of the complex stochastic processes in hydrology cycle owing to its associated non‐linearity and non‐stationarity characteristics. It an essential hydrological address time series nonlinear phenomena. In this research, a novel approach was proposed by integrating autoregressive conditionally heteroscedastic (ARCH) method with bootstrap model predict future intervals. For purpose, two located at Eastern Black Sea basin (Turkey) were subjected application methodology. Among other regression machine learning (ML) models, which are suitable for modeling, integrated moving average (ARIMA), seasonal (SARIMA), artificial neural network (ANN) selected modeling validation study. A group three numerical metrics graphical presentations used evaluation assessment. The ARCH performed superior mathematical interval prediction. Remarkable prediction accuracy shown against benchmark models. Overall, coupling procedure exhibited robust strategy predicting intervals suggested as new analysis tool.

Language: Английский

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