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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 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.

Язык: Английский

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

Environmental Pollution, Год журнала: 2024, Номер 351, С. 124040 - 124040

Опубликована: Апрель 27, 2024

Язык: 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, Год журнала: 2024, Номер 15(6), С. 102125 - 102125

Опубликована: Март 20, 2024

Язык: 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, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 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.

Язык: Английский

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

Journal of Hydrology, Год журнала: 2024, Номер 640, С. 131704 - 131704

Опубликована: Июль 20, 2024

Язык: 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, Год журнала: 2024, Номер 256, С. 124900 - 124900

Опубликована: Июль 30, 2024

Язык: 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, Год журнала: 2024, Номер 31(22), С. 32382 - 32406

Опубликована: Апрель 23, 2024

Язык: Английский

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Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

International Journal of Management Technology and Social Sciences, Год журнала: 2024, Номер unknown, С. 94 - 110

Опубликована: Май 20, 2024

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective management. Through a comprehensive review of the literature an in-depth analysis various AI ML techniques, this paper aims to put light cutting-edge approaches used in irrigation scheduling predictive modeling. The goal research is determine advantages, disadvantages, future directions ML-based management systems by means methodical algorithms, data sources, applications. Additionally, study seeks demonstrate how data-driven methods can enhance systems' sustainability, accuracy, precision. Stakeholders agriculture, resource management, conservation make well-informed decisions maximize techniques having thorough understanding theoretical underpinnings practical applications models. also attempts tackle issues like scalability, model interpretability, lack when implementing solutions for In final form, review's conclusions advance our use improve resilience efficiency, supporting adaptive sustainable strategies face rising scarcity concerns climate change. Design/Methodology/Approach: order gather information study, several articles from reliable sources were analyzed compared. Objective: To provide current gaps prediction models best suggest using fill these gaps. Results/ Findings: response growing challenges change, paper's findings highlight transformative potential optimizing scheduling, enhancing resilience, increasing strategies. Originality/Value: This uniqueness significance come its modeling ideal scheduling. It provides insights into new their possible optimization sustainability. Type Paper: Literature Review.

Язык: Английский

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Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(6), С. 2489 - 2519

Опубликована: Март 16, 2024

Abstract This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, precipitation. also accuracy multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it single MARS, M5 model tree (M5Tree), random forest (RF), multilayer perceptron neural network (MLP). In first stage, principal component is applied to diverse input combinations, both without lagged (Q), resulting twenty-three twenty respectively. Results demonstrate critical role Q for improved accuracy, as models exhibit significant performance degradation. second stage involves a comparative MARS-Kmeans other machine-learning models, utilizing best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms showcasing superior predicting streamflow.

Язык: Английский

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Assessment of machine learning models to predict daily streamflow in a semiarid river catchment

Neural Computing and Applications, Год журнала: 2024, Номер 36(21), С. 13087 - 13106

Опубликована: Апрель 24, 2024

Язык: Английский

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