Prediction of maximum scour depth in river bends by the Stacking model DOI Creative Commons
Junfeng Chen,

Zhou Xiao-quan,

Lirong Xiao

et al.

Journal of Hydroinformatics, Journal Year: 2023, Volume and Issue: 25(6), P. 2625 - 2642

Published: Nov. 1, 2023

Abstract The accurate prediction of maximum erosion depth in riverbeds is crucial for early protection bank slopes. In this study, K-means clustering analysis was used outlier identification and feature selection, resulting Plan 1 with six influential features. 2 included features selected by existing methods. Regression models were built using Support Vector Regression, Random Forest (RF Regression), eXtreme Gradient Boosting on sample data from 2. To enhance accuracy, a Stacking method feed-forward neural network introduced as the meta-learner. Model performance evaluated root mean squared error, absolute percentage R2 coefficients. results demonstrate that three outperformed 2, improvements values 0.0025, 0.0423, 0.0205, respectively. Among regression 1, RF performs best an value 0.9149 but still lower than 0.9389 achieved fusion model. Compared to formulas, model exhibits superior predictive performance. This study verifies effectiveness combining analysis, predicting scour bends, providing novel approach design.

Language: Английский

Innovative approaches to surface water quality management: advancing nitrate (NO3) forecasting with hybrid CNN-LSTM and CNN-GRU techniques DOI

Sina Davoudi,

Kiyoumars Roushangar

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 20, 2025

Language: Английский

Citations

2
A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting DOI
Saeed Khorram,

Nima Jehbez

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(10), P. 4097 - 4121

Published: May 29, 2023

Language: Английский

Citations

39
A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM DOI
Sajjad M. Vatanchi, Hossein Etemadfard, Mahmoud F. Maghrebi

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(12), P. 4769 - 4785

Published: Aug. 22, 2023

Language: Английский

Citations

37
Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study DOI Creative Commons
Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(8), P. e18506 - e18506

Published: July 20, 2023

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. deposit SSL restricts streamflow region, affecting aquatic life migration finally causing a river course shift. As result, data sediments their fluctuations are essential for number authorities especially decision makers. prediction often difficult due to issues such as site-specific data, models, lack several substantial components use in prediction, complexity its pattern. In past two decades, many machine learning algorithms have shown huge potential prediction. However, these models did not provide very reliable results, which led conclusion that accuracy should be improved. order solve concerns, this research proposes Long Short-Term Memory (LSTM) model proposed was applied Johor River located Malaysia. study allocated flow period 2010 2020. current research, four alternative models—Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), Short-term were investigated predict load. attained high correlation value between predicted actual (0.97), with minimum RMSE (148.4 ton/day MAE (33.43 ton/day).and can thus generalized application similar rivers around world.

Language: Английский

Citations

26
Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco DOI Creative Commons

Bouchra Bargam,

Abdelghani Boudhar, Christophe Kinnard

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 6(6)

Published: June 3, 2024

Abstract Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin Morocco, where are facing severe drought and demand continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) Random Forest (RF) against multiple linear (MLR) daily streamflow forecasting mountainous sub-basin of Rheraya between 2003 2016. results show that SVR performed best, followed by RF MLR. In measurable terms regarding mean performance, exhibited higher Nash–Sutcliffe efficiency score (NSE = 0.59) lower root squared error (RMSE 1.18 $$\text {m}^3\,\text {s}^{-1}$$ m 3 s - 1 ) compared to 0.53, RMSE MLR 0.54, 1.01 ). Furthermore,the available time series was too short properly capture full range variability, which reduced performance outside calibration conditions. These findings suggest ML algorithms, particularly SVR, can provide accurate estimation useful management when trained representative period. highlight capacity specifically augment enhanced resource arid regions.

Language: Английский

Citations

10
A novel application of transformer neural network (TNN) for estimating pan evaporation rate DOI Creative Commons

Mustafa Abed,

Monzur Alam Imteaz, Ali Najah Ahmed

et al.

Applied Water Science, Journal Year: 2022, Volume and Issue: 13(2)

Published: Dec. 30, 2022

Abstract For decision-making in farming, the operation of dams and irrigation systems, as well other fields water resource management hydrology, evaporation, a key activity throughout universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge creating accurate dependable predictive models is evaporation procedure's non-stationarity, nonlinearity, stochastic characteristics. This work examines, first time, transformer-based deep learning architecture prediction four different Malaysian regions. effectiveness proposed (DL) model, signified TNN, evaluated against two competitive reference DL models, namely Convolutional Neural Network Long Short-Term Memory, with regards to various statistical indices using monthly-scale dataset collected from meteorological stations 2000–2019 period. Using variety input variable combinations, impact every data on E p forecast also examined. performance assessment metrics demonstrate that compared benchmark frameworks examined this work, developed TNN technique was more precise modelling monthly loss owing evaporation. In terms effectiveness, enhanced self-attention mechanism, outperforms demonstrating potential use forecasting Relating application, model created projection offers estimate due can thus be used management, agriculture planning based irrigation, decrease fiscal economic losses farming related industries where consistent supervision estimation are considered necessary viable living economy.

Language: Английский

Citations

28
Improving sea level prediction in coastal areas using machine learning techniques DOI Creative Commons
Sarmad Dashti Latif, Mohammad Abdullah Almubaidin,

Chua Guang Shen

et al.

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(9), P. 102916 - 102916

Published: June 25, 2024

The objective of the current study is to investigate effectiveness specifically Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) models for sea level prediction. SVM kNN are compared using predicted data determined by machine learning model's performance. Thirteen were trained precisely properly throughout process. results showed that provide good performance during training process attained relatively poor testing On other hand, KNN model consistent both Regarding different kernels algorithm, Radial Basis Function (RBF) kernel most suitable, which provides finest analysis rise dataset acceptable values RSME, MAE, R2.

Language: Английский

Citations

4
A pioneering approach to deterministic rainfall forecasting for wet period in the Northern Territory of Australia using machine learning DOI Creative Commons
Rashid Farooq, Monzur Alam Imteaz,

Fatemeh Mekanik

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Extreme learning machine coupled with Heuristic algorithms for daily streamflow modeling at Lake Ziway Watershed, Ethiopia DOI
Gebre Gelete, Hüseyin Gökçekuş, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133345 - 133345

Published: April 1, 2025

Language: Английский

Citations

0
Deep learning reveals future streamflow characteristics change and climate sensitivity DOI

Subharthi Sarkar,

Mohd Imran Khan, Rajib Maity

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 660, P. 133457 - 133457

Published: May 5, 2025

Language: Английский

Citations

0