AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks

Alqahtani, Fehaid

doi:10.2166/wcc.2024.242

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Forecasting results using the LSTM model have shown darker results than other methods (Hayder et al 2022). In addition to hydrological forecasting, LSTM models are also applied to predict substance concentrations (Grenon et al 2022), wastewater flow control (Seshan et al 2024), and precipitation (Alqahtani 2024).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the river water environment carrying capacity using LSTM networks

Bui,

Tran,

Nguyen

2024

Water Supply

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River basins receive wastewater from socio-economic activities. In such a context, a comprehensive assessment and forecast of load-bearing capacity needs to be developed. This capacity depends on many complex factors, such as hydrology, hydraulics, and environment, leading to applying a modelling approach. This study aims to propose a hybrid modelling approach to evaluate and predict the river water environmental capacity (RWEC) in a basin. Big data technology with Python is applied to process modelling results and RWEC forecasts. The long short-term memory (LSTM) model predicts RWEC on each reach of the river channel network. The {RWECP, i, P = Nitrate, BOD, Phosphate, i = hourly} data set and related factors form a time series of data used for the LSTM forecasting model. Forecast results are evaluated through the root mean square error (RMSE) and mean absolute error (MAE). The results show that the average level over all 24 reaches for 7 forecast days: RMSENitrate = 22.16 (kg/day), RMSEBOD = 38.92 (kg/day), and RMSEPhosphate = 0.79 (kg/day). This is an acceptable result for a complex system and 7-day forecast. The results of the study help significantly with pollution control.

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Section: Introductionmentioning

confidence: 99%

Prediction of the river water environment carrying capacity using LSTM networks

Bui,

Tran,

Nguyen

2024

Water Supply

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Forecasting Rainfall: Evaluating Machine Learning Models on Australian Weather Data

Gupta,

Sharma,

Trivedi

2024

2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)

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Daily Runoff Prediction Based on FA-LSTM Model

Chai,

Zhang,

Tian

et al. 2024

Water

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Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

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AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks

Cited by 3 publications

References 43 publications

Prediction of the river water environment carrying capacity using LSTM networks

Prediction of the river water environment carrying capacity using LSTM networks

Forecasting Rainfall: Evaluating Machine Learning Models on Australian Weather Data

Daily Runoff Prediction Based on FA-LSTM Model

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