Monsoonal Rainfall Forecasting using LSTM Neural Network

Shweta, .; Sehrawat, Harkesh; Siwach, Vikas

doi:10.1109/icrito56286.2022.9964816

Cited by 2 publications

(1 citation statement)

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“…The experimental results showed that the LSTM method outperforms the BPNN in predicting precipitation. Shweta 15 uses LSTM over the monthly monsoon rainfall data of Haryana, Delhi and Chandigarh subdivision. The experiments show better results for non-linear rainfall data.…”

Section: Introductionmentioning

confidence: 99%

Hydrological Time Series Prediction based on IWOA-ALSTM

Zhang,

Cang,

Nedjah

et al. 2023

Preprint

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The prediction of hydrological time series is of great significance for the development of flood and drought prevention, and is an important research component of smart water resources. The nonlinear characteristics of the hydrological time series are an important factor affecting the accuracy of prediction. In order to better predict the non-linear component of the hydrological time series, (IWOA) used to tune long-short memory based neural network augmented with attention mechanisms (ALSTM). The proposed model is termed IWOA-ALSTM. The model hyperparameters setting has a significant impact on the prediction accuracy and operation efficiency. So, the improved whale optimization algorithm allows the dynamic adjustment of the required hyperparameters. The used network introduces an attention mechanism between the two layers of the LSTM, capable of adaptively focusing on different features according to each time unit in order to obtain information about the hydrological time series. In this work, we use the information about the non-linear water level, obtained from Hankou station, as experimentatl data. The results achieved by the proposed model, wherein IWOA, is exploited are compared to those yielded with using genetic algorithms, particle swarm optimization and whale optimization algorithm. We demonstrate that IWOA-ALSTM provides a better performance.

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Section: Introductionmentioning

confidence: 99%

Hydrological Time Series Prediction based on IWOA-ALSTM

Zhang,

Cang,

Nedjah

et al. 2023

Preprint

View full text Add to dashboard Cite

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Hydrological time series prediction based on IWOA-ALSTM

Zhang,

Cang,

Nedjah

et al. 2024

Sci Rep

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The prediction of hydrological time series is of great significance for developing flood and drought prevention approaches and is an important component in research on smart water resources. The nonlinear characteristics of hydrological time series are important factors affecting the accuracy of predictions. To enhance the prediction of the nonlinear component in hydrological time series, we employed an improved whale optimisation algorithm (IWOA) to optimise an attention-based long short-term memory (ALSTM) network. The proposed model is termed IWOA-ALSTM. Specifically, we introduced an attention mechanism between two LSTM layers, enabling adaptive focus on distinct features within each time unit to gather information pertaining to a hydrological time series. Furthermore, given the critical impact of the model hyperparameter configuration on the prediction accuracy and operational efficiency, the proposed improved whale optimisation algorithm facilitates the discovery of optimal hyperparameters for the ALSTM model. In this work, we used nonlinear water level information obtained from Hankou station as experimental data. The results of this model were compared with those of genetic algorithms, particle swarm optimisation algorithms and whale optimisation algorithms. The experiments were conducted using five evaluation metrics, namely, the RMSE, MAE, NSE, SI and DR. The results show that the IWOA is effective at optimising the ALSTM and significantly improves the prediction accuracy of nonlinear hydrological time series.

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Monsoonal Rainfall Forecasting using LSTM Neural Network

Cited by 2 publications

References 10 publications

Hydrological Time Series Prediction based on IWOA-ALSTM

Hydrological Time Series Prediction based on IWOA-ALSTM

Hydrological time series prediction based on IWOA-ALSTM

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