Estimation of Water Disinfection by Using Data Mining

Bashayreh, Esra'a; Manasrah, Ahmad; Alkhalil, Shahnaz

doi:10.12912/27197050/132088

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…With the advances in computing capacity, the need for improved accuracy, and reduced complexity, machine learning techniques have emerged as a promising horizon. Thus, an increasing tendency toward deep learning has been noticed with a particular interest in Artificial Neural Networks (ANNs), and it was extensively utilized by researchers in a variety of applications (Alardhi et al, 2023; Babu et al, 2022;Bashayreh et al, 2021;Chen et al, 2023;Oni et al, 2022). In the field of hydrology, ANNs derive their strength from their adaptability and ability to perceive complex and intricate connections between the variables, which is essential for simulating the inherent complexity and non-linearity of the hydrological systems (Govindaraju and Rao, 2000; Wu and Chau, 2011).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Runoff Prediction: Evaluating the Long-Short-Term Memory Neural Network Architectures for Capturing Extreme Discharge Events in the Ouergha Basin, Morocco

Karmouda,

Bouramtane,

TAHIRI

et al. 2023

Ecol. Eng. Environ. Technol.

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Rainfall-runoff modeling plays a crucial role in achieving efficient water resource management and flood forecasting, particularly in the context of increasing intensity and frequency of extreme meteorological events induced by climate change. Therefore, the aim of this research is to assess the accuracy of the Long-Short-Term Memory (LSTM) neural networks and the impact of its architecture in predicting runoff, with a particular focus on capturing extreme hydrological discharges in the Ouergha basin; a Moroccan Mediterranean basin with historical implications in many cases of flooding; using solely daily rainfall and runoff data for training. For this purpose, three LSTM models of different depths were constructed, namely LSTM 1 single-layer, LSTM 2 bi-layer, and LSTM 3 tri-layer, their window size and hyperparameters were first tuned, and on seven years of daily data they were trained, then validated and tested on two separate years to ensure the generalization on unseen data. The performance of the three models was compared using hydrogram-plots, Scatter-plots, Taylor diagrams, and several statistical metrics. The results indicate that the single-layer LSTM 1 outperforms the other models, it consistently achieves higher overall performance on the training, validation, and testing periods with a coefficient of determination R-squared of 0.92, 0.97, and 0.95 respectively; and with Nash-Sutcliffe efficiency metric of 0.91, 0.94 and 0.94 respectively, challenging the conventional beliefs about the direct link between complexity and effectiveness. Furthermore, all the models are capable of capturing the extreme discharges, although, with a moderate underprediction trend for LSTM 1 and 2 as it does not exceed -25% during the test period. For LSTM 3, even if its underestimation is less pronounced, its increased error rate reduces the confidence in its performance. This study highlights the importance of aligning model complexity with data specifications and suggests the necessity of considering unaccounted factors like upstream dam releases to enhance the efficiency in capturing the peaks of extreme events.

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