Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Noor, Fahima; Haq, Sanaulla; Rakib, Mohammed; Ahmed, Tarik; Jamal, Zeeshan; Siam, Zakaria Shams; Hasan, Rubyat Tasnuva; Adnan, Mohammed Sarfaraz Gani; Dewan, Ashraf; Rahman, Rashedur M.

doi:10.3390/w14040612

Cited by 44 publications

(11 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simple time series prediction easily ignores the spatial information of the data. In [25], the authors considered the rainfall and fow at diferent stations, combined with the latitude and longitude information of the target area. Tey used a spatiotemporal attention model integrated with LSTM to improve food prediction performance further.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Ni,

Fam,

Marsani

2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Flood disasters occur worldwide, and flood risk prediction is conducive to protecting human life and property safety. Influenced by topographic changes and rainfall, the water level fluctuates randomly and violently during the flood, introducing many noises and directly increasing the difficulty of flood prediction. A data-driven flood forecasting method is proposed based on data preprocessing and a two-layer BiLSTM-Attention network to improve forecast accuracy. First, the Variational Mode Decomposition (VMD) is used to decompose the data for reducing noise and produce suitable Intrinsic Mode Functions (IMFs); Then, an optimized two-layer attention-based Bidirectional Long Sshort-Term memory (BiLSTM-Attention) network is constructed to predict each IMF. Finally, two optimization algorithms are used to obtain the optimized parameters of VMD and BiLSTM intelligently, increasing the self-adaptability. The inertia factor of particle swarm optimization is improved and then used to optimize the five hyperparameters of BiLSTM. The proposed model reduces storage errors for smaller training sets and can achieve good performance. Three water level data sets from the Yangtze River in China are used for comparative experiments. Numerical results show that the peak height absolute error is within 2 cm, and the relative error of peak time arrival is within 30%. Compared with LSTM, BiLSTM, CNN-BiLSTM-attention, etc., the proposed model reduces the root mean square error by at least 50% and has advantages for high-risk forecasting when the water level exceeds the defense line and fluctuates prominently.

show abstract

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Ni,

Fam,

Marsani

2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In [12] the study concentrates on using a combined deep learning method, specifically a CNN-LSTM model, to predict both water level and water quality simultaneously. This hybrid architecture incorporates convolutional neural networks (CNN) for extracting spatial features and long short-term memory (LSTM) networks to capture temporal dependencies within the data, in [13]this paper focuses on integrating a hybrid CNN-LSTM deep learning model with a boundary-corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting in [14] this article focuses on water level forecasting using a spatiotemporal attention-based long short-term memory (LSTM) network, in [15]this paper introduces a hybrid method for basin water level prediction, utilizing Long Short-Term Memory (LSTM) networks and precipitation data.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Hydrologycal Modelling based on Long Short Term Memory Networks for Water Level Forecasting.

Renteria - Mena,

Plaza,

Giraldo

2024

Preprint

View full text Add to dashboard Cite

In this work, a novel approach is presented for the multivariate prediction of hydrological variables, employing a specialized type of recurrent neural network (RNN) known as the long-term memory network (LSTM).The central objective is to develop a flood prediction model using data collected from two hydrological stations situated along the Atrato River in the Choc´o Department, Colombia. The interconnected variables of water level, water flow, and water precipitation are analyzed in this model to enhance flood prediction accuracy. The LSTM model, integrated into an RNN structure, is designed to capture the complex dynamics and cross-correlations inherent in these hydrological variables. Validation involves a comparison with linear and nonlinear Nonlinear AutoRegressive with eXogenous input (NARX) models, considering factors such as estimation error and computational time. Additionally, the study conducts further analysis for water level prediction under two scenarios: utilizing only outflow and inflow measurements or predicting outflow alongside measuring inflow, with the objective of utilizing the proposed approach as a flood early warning system. &dagger; This article is a revised and expanded version of a paper entitled Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting, which was presented at International Conference on Time Series and Forecasting (ITISE-2023), Gran Canaria, Spain, 12th-14th July, 2023.

show abstract

“…Furthermore, stochastic models adapt to the non-linearity of hydrological processes and address uncertainties in parameter estimations. [11,12]. On the other hand, stochastic models introduce various techniques for flood estimation, ranging from simple regression of discharge to detailed modeling of hydrological processes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

Dabire,

Ezin,

Firmin

2024

Preprint

View full text Add to dashboard Cite

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

show abstract

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Cited by 44 publications

References 50 publications

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction

Multivariate Hydrologycal Modelling based on Long Short Term Memory Networks for Water Level Forecasting.

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

Contact Info

Product

Resources

About