Related Concept Videos
01:35Long-term Potentiation
56.9K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
56.9K
01:03Prediction Intervals
2.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.6K
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Sort by
Same author
Enhancing groundwater recharge mapping in arid regions with geospatial multi-criteria analysis in the Eastern desert of Egypt.
Scientific reports·2026
Related Experiment Video
Updated: Nov 9, 2025
10:32
Image-based Lagrangian Particle Tracking in Bed-load Experiments
Published on: July 20, 2017
9.2K
Suspended sediment load prediction using long short-term memory neural network.
Nouar AlDahoul1, Yusuf Essam2, Pavitra Kumar3
1Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Malaysia.
Scientific Reports
|April 10, 2021
Summary
Predicting suspended sediment concentration (SSC) is vital for river management. A Long Short-Term Memory model accurately forecasts SSC in Malaysia
Area of Science:
- Environmental Science
- Hydrology
- Data Science
Background:
- Riverine suspended sediment deposition significantly impacts environmental health, agriculture, and water resources.
- Accurate forecasting of suspended sediment concentration (SSC) is crucial for effective hydraulic structure operation and river management.
- Predicting SSC is complex due to data limitations and intricate patterns.
Purpose of the Study:
- To develop and evaluate a Long Short-Term Memory (LSTM) model for predicting SSC in Malaysia's Johor River.
- To assess the model's performance using only river discharge data.
- To compare LSTM against other machine learning models for SSC prediction.
Main Methods:
- Utilized a decade of historical discharge and SSC data (1988-1998) from the Johor River.
- Developed and tested four predictive models: ElasticNet Linear Regression, Multi-Layer Perceptron, Extreme Gradient Boosting, and Long Short-Term Memory.
- Evaluated model performance across daily, weekly, 10-daily, and monthly prediction scenarios.
Main Results:
- The Long Short-Term Memory model demonstrated superior performance compared to other tested models.
- LSTM achieved high regression values: 92.01% (daily), 96.56% (weekly), 96.71% (10-daily), and 99.45% (monthly).
- The study successfully predicted SSC using a single input variable (discharge).
Conclusions:
- LSTM is a highly effective tool for forecasting suspended sediment concentration, even with limited input data.
- The findings provide valuable insights for water resource management and hydraulic engineering in river systems.
- This approach offers a robust solution for addressing the challenges in SSC prediction.