A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting

Water Resources Management, Год журнала: 2023, Номер 37(10), С. 4097 - 4121

Опубликована: Май 29, 2023

Язык: Английский

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A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM

Water Resources Management, Год журнала: 2023, Номер 37(12), С. 4769 - 4785

Опубликована: Авг. 22, 2023

Язык: Английский

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Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study

Heliyon, Год журнала: 2023, Номер 9(8), С. e18506 - e18506

Опубликована: Июль 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.

Язык: Английский

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

Deleted Journal, Год журнала: 2024, Номер 6(6)

Опубликована: Июнь 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.

Язык: Английский

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

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(2)

Опубликована: Янв. 20, 2025

Язык: Английский

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A novel application of transformer neural network (TNN) for estimating pan evaporation rate

Applied Water Science, Год журнала: 2022, Номер 13(2)

Опубликована: Дек. 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.

Язык: Английский

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Improving sea level prediction in coastal areas using machine learning techniques

Ain Shams Engineering Journal, Год журнала: 2024, Номер 15(9), С. 102916 - 102916

Опубликована: Июнь 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.

Язык: Английский

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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm

Water Resources Management, Год журнала: 2023, Номер 37(10), С. 3953 - 3972

Опубликована: Июнь 9, 2023

Язык: Английский

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Modeling soil loss under rainfall events using machine learning algorithms

Journal of Environmental Management, Год журнала: 2024, Номер 352, С. 120004 - 120004

Опубликована: Янв. 12, 2024

Язык: Английский

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Predicting soil loss in small watersheds under different emission scenarios from CMIP6 using random forests

Earth Surface Processes and Landforms, Год журнала: 2024, Номер unknown

Опубликована: Сен. 6, 2024

Abstract Soil loss is a common land degradation process worldwide, which impacted by use and climate change. In this study, random forests (RF) were first used to establish soil model at the scale of small watershed in hilly‐gully region Loess Plateau based on field observation data. Subsequently, was predict Chabagou under historical (1990–2020) future emission scenarios, namely SSP1–2.6 (low‐emission), SSP2–4.5 (medium‐emission) SSP5–8.5 (high‐emission) (2030–2,100) from Coupled Model Intercomparison Project Phases 6 (CMIP6). RF model, coefficient determination (R 2 ) Nash‐Sutcliffe efficiency (NS) both greater than 0.86, RMSE‐observations standard deviation ratio (RSR) less 0.36. Additionally, RF‐based had higher simulation accuracy robustness those previous models, indicating its potential for wider applications simulating loss. Compared with between 1990 1999, change led 35.36% increase loss, while resulted an 11.13% reduction 2000 2020 watershed. This reveals that current management could not effectively counterbalance caused rainstorms. Furthermore, compared period (1990–2020), SSP1–2.6, (2030–2,100), rates without would be increased 6.01%, 19.11% 35.35%, changed −5.88%, +4.41% +19.12%, respectively. These results help provide scientific basis enhancing capacity respond mitigation water Plateau.

Язык: Английский

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