Stream-Flow Forecasting of Small Rivers Based on LSTM

• URL: http://arxiv.org/abs/2001.05681v1
• Date: Thu, 16 Jan 2020 07:14:32 GMT
• Title: Stream-Flow Forecasting of Small Rivers Based on LSTM
• Authors: Youchuan Hu, Le Yan, Tingting Hang and Jun Feng
• Abstract summary: This paper tries to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model. We collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)
• Score: 3.921808417990452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stream-flow forecasting for small rivers has always been of great importance, yet comparatively challenging due to the special features of rivers with smaller volume. Artificial Intelligence (AI) methods have been employed in this area for long, but improvement of forecast quality is still on the way. In this paper, we tried to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model, which aims in the field of time-series data. Utilizing LSTM, we collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data from that hydrologic station 6 hours in the future. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R^2). By comparing LSTM's prediction with predictions of Support Vector Regression (SVR) and Multilayer Perceptions (MLP) models, we showed that LSTM has better performance, achieving RMSE of 82.007, MAE of 27.752, and R^2 of 0.970. We also did extended experiments on LSTM model, discussing influence factors of its performance.

Related papers

• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more-efficient metric for performance estimation. We extend the power law analytical function to predict domain-specific pre-training loss based on FLOPs across data sources. We employ a two-layer neural network to model the non-linear relationship between multiple domain-specific loss and downstream performance.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM [0.0]
  We extend the application of CNN-LSTMs to time series settings, leveraging lagged streamflow data to predict streamflow. Our results show a substantial improvement in predictive performance in 21 out of 32 HUC8 basins in Nebraska.
  arXiv  Detail & Related papers  (2024-04-11T17:10:57Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Scaling Relationship on Learning Mathematical Reasoning with Large Language Models [75.29595679428105]
  We investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that rejection samples from multiple models push LLaMA-7B to an accuracy of 49.3% on GSM8K which outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly.
  arXiv  Detail & Related papers  (2023-08-03T15:34:01Z)
• Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
  Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
  arXiv  Detail & Related papers  (2023-07-29T22:49:50Z)
• Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood [11.416877401689735]
  We show that a machine learning model that is well-trained on a minimal amount of data, can help predict the long-term dynamic behavior of a one-dimensional dam-break flood with satisfactory accuracy. We demonstrate a good prediction ability of the RC-ESN model, which ahead predicts wave propagation behavior 286 time-steps in the dam-break flood with a root mean square error (RMSE) smaller than 0.01.
  arXiv  Detail & Related papers  (2022-09-19T02:58:31Z)
• On Continual Model Refinement in Out-of-Distribution Data Streams [64.62569873799096]
  Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams. Existing continual learning (CL) problem setups cannot cover such a realistic and complex scenario. We propose a new CL problem formulation dubbed continual model refinement (CMR)
  arXiv  Detail & Related papers  (2022-05-04T11:54:44Z)
• Optimal Reservoir Operations using Long Short-Term Memory Network [3.680403821470857]
  Real-time inflow forecast helps in efficient operation of water resources. This work proposes a naive anomaly detection algorithm baseline based on LSTM. Experiments are run on data from Bhakra Dam Reservoir in India.
  arXiv  Detail & Related papers  (2021-09-07T18:16:22Z)
• Machine Learning for Postprocessing Ensemble Streamflow Forecasts [0.0]
  We integrate dynamical modeling with machine learning to demonstrate the enhanced quality of streamflow forecasts at short-to medium-range (1 - 7 days) We employ a Long Short-Term Memory (LSTM) neural network to correct forecast biases in raw ensemble streamflow forecasts obtained from dynamical modeling. The verification results show that the LSTM can improve streamflow forecasts relative to climatological, temporal persistence, deterministic, and raw ensemble forecasts.
  arXiv  Detail & Related papers  (2021-06-15T18:46:30Z)
• Learning representations with end-to-end models for improved remaining useful life prognostics [64.80885001058572]
  The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T16:45:18Z)
• High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks [0.03694429692322631]
  The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time.
  arXiv  Detail & Related papers  (2020-02-07T00:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.