Deep insight into daily runoff forecasting based on a CNN-LSTM model

Deng, Huiqi; Chen, Wenjie; Huang, Guoru

doi:10.1007/s11069-022-05363-2

Cited by 33 publications

(7 citation statements)

References 40 publications

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“…This superiority underscores the capacity of deep learning to extract intricate patterns and features from complex data, enabling more accurate anomaly detection in network traffic.  LSTM demonstrates strong performance in capturing temporal dependencies within the data, thereby boosting detection accuracy [16]. By incorporating recurrent connections, LSTM effectively learns and remembers long-range dependencies in sequential data, which is particularly beneficial for detecting anomalous patterns evolving over time in network traffic.…”

Section: ) Discussionmentioning

confidence: 99%

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Arjunan

2024

IJRASET

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In light of the increasing sophistication of cyberattacks and the rapid growth in network traffic, it is essential to detect network traffic anomalies or intrusions as they occur. Manual inspection is inefficient due to the large volume, speed, and variety network traffic data. This paper suggests using deep learning techniques in order to build intelligent models which can detect network traffic anomalies automatically within big data environments. We present a framework for anomaly detection using long-short-term memory models (LSTM) and convolutional neural network (CNN). The models are based on data extracted from packet captures. The models are evaluated on benchmark intrusion datasets as well as a large scale real network traffic dataset. The results show that deep learning models are able to detect anomalies more effectively than traditional shallow learning methods. Models can handle high-volume streaming data with low latency and in real time. To improve detection efficiency, we also propose optimization methods such as model compression and transfer learning. This work shows the effectiveness of deep learning for real-time anomaly detection within big data environments

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Section: ) Discussionmentioning

confidence: 99%

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Arjunan

2024

IJRASET

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“…For Model 1, Model 4, and Model 7, the numbers of samples required for building the models were 42, 155, and 37, respectively. The differences in model performance between the training and testing datasets can be considered as indicators as to whether the model had been overfitted, which is a common phenomenon when using machine learning models [44,45]. The results show that Model 4 performed more consistently between the training and testing dataset than Model 1 and Model 7, implying that a larger number of samples will lead to a decrease in the degree of overfitting.…”

Section: Direction To Future Studies For Applying Machine Learning To...mentioning

confidence: 96%

Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method

Wang,

Sun,

et al. 2023

Remote Sensing

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River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR/RSWIR (Model 5), and a combination of AREA_SAR and RNIR/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR/RSWIR (Model 6) were 0.25 and 56.5 m3/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions.

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“…Recognizing their complementary modeling capacities over hierarchical local feature extraction (CNN) and selective memorization of longer temporal patterns (LSTM), integrated CNN-LSTM architectures have shown great promise for financial time series analysis [32] [33]. Typically, contracted CNN representations of local variable-wise patterns feed into subsequent LSTMs to assimilate both short and long-range historical contexts.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Nelson et al [37] proposed a character-level language model with event-based trading. [32] [33] evaluated combinations of CNN and LSTMs showing improvements over individual models. However, these deep neural models often face challenges with longer-term dependencies in sequential data due to vanishing gradient issues.…”

Section: Deep Neural Networkmentioning

confidence: 99%

The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting

Phien,

Platos

2023

IJACSA

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Accurate stock market forecasting has remained an elusive endeavor due to the inherent complexity of financial systems dynamics. While deep neural networks have shown initial promise, robustness concerns around long-term dependencies persist. This research pioneers a synergistic fusion of nonlinear time series analysis and algorithmic advances in representation learning to enhance predictive modeling. Phase space reconstruction provides a principled way to reconstruct multidimensional phase spaces from single variable measurements, elucidating dynamical evolution. Transformer networks with self-attention have recently propelled state-of-the-art results in sequence modeling tasks. This paper introduces PSR-Transformer Networks specifically tailored for stock forecasting by feeding PSR interpreted constructs to transformer encoders. Extensive empirical evaluation on 20 years of historical equities data demonstrates significant accuracy improvements along with enhanced robustness against LSTM, CNN-LSTM and Transformer models. The proposed interdisciplinary fusion establishes new performance benchmarks on modeling financial time series, validating synergies between domain-specific reconstruction and cutting-edge deep learning.

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Deep insight into daily runoff forecasting based on a CNN-LSTM model

Cited by 33 publications

References 40 publications

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models

Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method

The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting

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