Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region DOI Creative Commons
Emad Elabd,

Hany Mohamed Hamouda,

Mazen Ali

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 10, 2025

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems cities. It has worldwide economic consequences. change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With focus on Al-Qassim Region, Saudi Arabia, assesses temperature, air dew point, visibility distance, atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) reduce dataset imbalance. The CNN-GRU-LSTM was compared 5 classic regression models: DTR, RFR, ETR, BRR, K-Nearest Neighbors. Five main measures were evaluate performance: MSE, MAE, MedAE, RMSE, R². After Min-Max normalization, split into training (70%), validation (15%), testing (15%) sets. paper shows beats standard methods in all four climatic scenarios, R² values 99.62%, 99.15%, 99.71%, 99.60%. Deep predicts climate well can guide environmental policy urban development decisions.

Language: Английский

A review of hybrid deep learning applications for streamflow forecasting DOI
Kin‐Wang Ng, Yuk Feng Huang, Chai Hoon Koo

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130141 - 130141

Published: Sept. 12, 2023

Language: Английский

Citations

81
River stream flow prediction through advanced machine learning models for enhanced accuracy DOI Creative Commons
Naresh Kedam, Deepak Kumar Tiwari, Vijendra Kumar

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102215 - 102215

Published: May 4, 2024

The Narmada River basin, a significant water resource in central India, plays crucial role supporting agricultural, industrial, and domestic supply. Effective management of this basin requires accurate streamflow forecasting, which has become increasingly important. This study delves into forecasting using historical data from five major river stations, covering the upper reaches East middle sections. dataset spans 1978 to 2020 undergoes rigorous screening preparation, including normalization StandardScaler. research adopts comprehensive approach, developing models for training on 70% data, validation most current 15%, testing against future targets with another 15% data. To achieve precise predictions, suite machine learning is employed, CatBoost, LGBM (Light Gradient Boosting Machine), Random Forest, XGBoost. Performance evaluation these relies key indices such as mean squared error (MSE), absolute (MAE), root square (RMSE), percent (RMSPE), normalized (NRMSE), R-squared (R2). Notably, among models, Forest emerges robust prediction, showcasing its effectiveness handling complexities hydrological forecasting. contributes significantly field by providing insights performance various models. findings not only enhance our understanding watershed dynamics but also highlight pivotal that can play improving sustainable management.

Language: Английский

Citations

20
Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion DOI
Zuxiang Situ, Qi Wang, Shuai Teng

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130743 - 130743

Published: Jan. 26, 2024

Language: Английский

Citations

17
A comprehensive study of deep learning for soil moisture prediction DOI Creative Commons
Yanling Wang, Liangsheng Shi,

Yaan Hu

et al.

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(4), P. 917 - 943

Published: Feb. 27, 2024

Abstract. Soil moisture plays a crucial role in the hydrological cycle, but accurately predicting soil presents challenges due to nonlinearity of water transport and variability boundary conditions. Deep learning has emerged as promising approach for simulating dynamics. In this study, we explore 10 different network structures uncover their data utilization mechanisms maximize potential deep prediction, including three basic feature extractors seven diverse hybrid structures, six which are applied prediction first time. We compare predictive abilities computational costs models across textures depths systematically. Furthermore, exploit interpretability gain insights into workings attempt advance our understanding For forecasting, results demonstrate that temporal modeling capability long short-term memory (LSTM) is well suited. improved accuracy achieved by attention LSTM (FA-LSTM) generative-adversarial-network-based (GAN-LSTM), along with Shapley (SHAP) additive explanations analysis, help us discover effectiveness benefits adversarial training extraction. These findings provide effective design principles. The values also reveal varying leveraging approaches among models. t-distributed stochastic neighbor embedding (t-SNE) visualization illustrates differences encoded features summary, comprehensive study provides highlights importance appropriate model specific tasks. hope work serves reference studies other hydrology problems. codes 3 machine open source.

Language: Английский

Citations

17
Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm DOI Creative Commons
Fabio Di Nunno, Giovanni de Marinis, Francesco Granata

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 29, 2023

In recent years, the growing impact of climate change on surface water bodies has made analysis and forecasting streamflow rates essential for proper planning management resources. This study proposes a novel ensemble (or hybrid) model, based combination Deep Learning algorithm, Nonlinear AutoRegressive network with eXogenous inputs, two Machine algorithms, Multilayer Perceptron Random Forest, short-term forecasting, considering precipitation as only exogenous input forecast horizon up to 7 days. A large regional was performed, 18 watercourses throughout United Kingdom, characterized by different catchment areas flow regimes. particular, predictions obtained Learning-Deep model were compared ones achieved simpler models an both algorithms algorithm. The hybrid outperformed models, values R2 above 0.9 several watercourses, greatest discrepancies small basins, where high non-uniform rainfall year makes rate challenging task. Furthermore, been shown be less affected reductions in performance increases leading reliable even 7-day forecasts.

Language: Английский

Citations

39
Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin DOI
Bülent Haznedar, Hüseyin Çağan Kılınç, Furkan Ozkan

et al.

Natural Hazards, Journal Year: 2023, Volume and Issue: 117(1), P. 681 - 701

Published: Feb. 24, 2023

Language: Английский

Citations

24
A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning DOI Open Access
Xinfeng Zhao, Hongyan Wang,

Mingyu Bai

et al.

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1407 - 1407

Published: May 15, 2024

Artificial intelligence has undergone rapid development in the last thirty years and been widely used fields of materials, new energy, medicine, engineering. Similarly, a growing area research is use deep learning (DL) methods connection with hydrological time series to better comprehend expose changing rules these series. Consequently, we provide review latest advancements employing DL techniques for forecasting. First, examine application convolutional neural networks (CNNs) recurrent (RNNs) forecasting, along comparison between them. Second, made basic enhanced long short-term memory (LSTM) analyzing their improvements, prediction accuracies, computational costs. Third, performance GRUs, other models including generative adversarial (GANs), residual (ResNets), graph (GNNs), estimated Finally, this paper discusses benefits challenges associated forecasting using techniques, CNN, RNN, LSTM, GAN, ResNet, GNN models. Additionally, it outlines key issues that need be addressed future.

Language: Английский

Citations

10
Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin DOI
Levent Latifoğlu, Savaş Bayram, Gaye Aktürk

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Language: Английский

Citations

9
River discharge prediction based multivariate climatological variables using hybridized long short-term memory with nature inspired algorithm DOI
Sandeep Samantaray, Abinash Sahoo, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132453 - 132453

Published: Dec. 1, 2024

Language: Английский

Citations

9
Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization DOI
Hüseyin Çağan Kılınç, Iman Ahmadianfar, Vahdettin Demir

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(9), P. 3699 - 3714

Published: May 3, 2023

Language: Английский

Citations

18