ReviewMethodsX2024
A critical review of RNN and LSTM variants in hydrological time series predictions.
Review in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed.
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- Deep recurrent neural networks for water hammer transient prediction and dynamic protection optimization in long distance pipelines.Scientific reports · 2026Article
- Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability.EcoHealth · 2026Review
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Pre-trained multi-scale RWKV-GCN for multivariate time series forecasting.Scientific reports · 2026Article
- Intelligent educational decision-making system driven by multimodal data fusion and knowledge graphs.Scientific reports · 2026Article
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- Analysis of the performance of LSTM-DNN models with the consideration of signal complexity in milling processes.Journal of intelligent manufacturing · 2026Article
- Hybrid fuzzy clustering and temporal deep learning framework for multi-parameter forecasting in industrial thermal processes.Frontiers in artificial intelligence · 2026Article
- Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation.Frontiers in neurorobotics · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- From prediction to sustainability: AI for smart energy management in wastewater treatment plants.Scientific reports · 2025Article
- On the recurrent neural network model with robust expectile-based loss function in economic data forecasting.MethodsX · 2025Article
- Attention-driven deep learning models for multivariate time series forecasting of reservoir water levels.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement in Artificial Intelligence (AI) and big data has developed significance in the water sector, particularly in hydrological time-series predictions. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have become research focal points due to their effectiveness in modeling non-linear, time-variant hydrological systems. This review explores the different architectures of RNNs, LSTMs, and Gated Recurrent Units (GRUs) and their efficacy in predicting hydrological time-series data.•RNNs are foundational but face limitations such as vanishing gradients, which impede their ability to model long-term dependencies. LSTMs and GRUs have been developed to overcome these limitations, with LSTMs using memory cells and gating mechanisms, while GRUs provide a more streamlined architecture with similar benefits.•The integration of attention mechanisms and hybrid models that combine RNNs, LSTMs, and GRUs with other Machine learning (ML) and Deep Learning (DL) has improved prediction accuracy by capturing both temporal and spatial dependencies.•Despite their effectiveness, practical implementations of these models in hydrological time series prediction require extensive datasets and substantial computational resources. Future research should develop interpretable architectures, enhance data quality, incorporate domain knowledge, and utilize transfer learning to improve model generalization and scalability across diverse hydrological contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.