A review of models for water level forecasting based on machine learning

Wee, Wei Joe; Zaini, Nur’atiah; Ahmed, Ali Najah; El‐Shafie, Ahmed

doi:10.1007/s12145-021-00664-9

Cited by 34 publications

(8 citation statements)

References 72 publications

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“…The methods range from traditional machine learning approaches to those that rely on deep learning (DL) algorithms. Support Vector Machines (SVM) have been widely used by researchers [24]- [26].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Rainfall Prediction Using Deep Learning Algorithms

Huthaifa Abuhammad

2024

jes

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Water scarcity presents a significant challenge for many countries. Given that rainfall serves as the primary source of drinking water, irrigation, and various other purposes, the accuracy of rainfall prediction holds utmost importance for decision-makers. Climate change has exacerbated the unpredictability of rainfall patterns, posing challenges to sustainable water resource management. Artificial Intelligence (AI) techniques, especially deep neural networks, have demonstrated effectiveness in predicting rainfall patterns and enhancing water resource management. This study involved reprocessing data obtained from the Jordanian Ministry of Water and Irrigation Jordan Valley Authority to create a dataset. The dataset was then evaluated using Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), and Support Vector Machine (SVM) models for rainfall prediction. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared were employed, revealing the reliability of the forecasting models.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Rainfall Prediction Using Deep Learning Algorithms

Huthaifa Abuhammad

2024

jes

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“…For water resource management, power generation, and drought prevention, the accurate forecasting of water levels in reservoirs is of great importance. Hybrid metaheuristic algorithms, including ANN, ANFIS, BA, COA, and SVM, have been employed to identify factors and challenges in water level prediction from 2000 to 2020 [234]. To water management planning in drought-prone regions like the American West, the results show that RF provides the most accurate results and reduces modeling run times, enabling exploration of future climate changes and drought conditions [235].…”

Section: Using ML For Water Activitiesmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Drogkoula,

Kokkinos,

Samaras

2023

Applied Sciences

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This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

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“…Some deep learning networks are born to handle data with time steps, RNN (recurrent neural network) for instance, their hidden states can be passed along time steps, and RNNs (RNN and its variants) are successfully employed to various kinds of hydrology tasks [18][19][20][21][22][23][24][25]. In particular, to deal with short-term runoff prediction problems, a deep learning multi-dimensional ensemble method has proven to be effective [26,27].…”

Section: Introductionmentioning

confidence: 99%

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

Liu,

Zou,

Jiang

et al. 2023

Water

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Accurately computing river discharge is crucial, but traditional computing methods are complex and need the assistance of many other hydraulic parameters. Therefore, it is of practical value to develop a convenient and effective auto-computation technique for river discharge. Water surface elevation is relatively easy to obtain and there is a strong relationship between river discharge and water surface elevation, which can be used to compute river discharge. Unlike previous usage of deep learning to predict short-term river discharge that need multiple parameters besides water level, this paper proved that deep learning has the potential to accurately compute long-term river discharge purely based on water level. It showed that the majority of relative errors on the test dataset were within ±5%, particularly it could operate continuously for almost one year with high precision without retraining. Then, we used BiGRU to compute river flow with different hyperparameters, and its best RMSE, NSE, MAE, and MAPE values were 256 m3/s, 0.9973, 207 m3/s, and 0.0336, respectively. With this data-driven based technology, it will be more convenient to obtain river discharge time series directly from local water surface elevation time series accurately in natural rivers, which is of practical value to water resources management and flood protection.

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A review of models for water level forecasting based on machine learning

Cited by 34 publications

References 72 publications

Rainfall Prediction Using Deep Learning Algorithms

Rainfall Prediction Using Deep Learning Algorithms

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

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