Limnological Review

18 pages, 8606 KiB

Open AccessArticle

Design and Implementation of a Deep Learning Model and Stochastic Model for the Forecasting of the Monthly Lake Water Level

by Waleed Ahmed Hassen Al-Nuaami, Lamiaa Abdul-jabbar Dawod, B. M. Golam Kibria and Shahryar Ghorbani

Limnol. Rev. 2024, 24(3), 217-234; https://doi.org/10.3390/limnolrev24030013 - 10 Jul 2024

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Abstract

Freshwater is becoming increasingly vulnerable to pollution due to both climate change and an escalation in water consumption. The management of water resource systems relies heavily on accurately predicting fluctuations in lake water levels. In this study, an artificial neural network (ANN), a deep learning (DL) neural network model, and an autoregressive integrated moving average (ARIMA) model were employed for the water level forecasting of the St. Clair and Ontario Lakes from 1981 to 2021. To develop the models, we utilized the average mutual information and incorporated lag periods of up to 6 months to identify the optimal inputs for the water level assessment in the lakes. The results were compared in terms of the root mean square error (RMSE), coefficient of correlation (r), and mean absolute percentage error (MAPE) and graphical criteria. Upon evaluating the results, it was observed that the error values for the deep learning models were insignificant at the designated stations: Lake St. Clair—0.16606 m < RMSE < 1.0467 m and Lake Ontario—0.0211 m < RMSE < 0.7436 m. The developed deep learning model increased the accuracy of the models by 5% and 3.5% for Lake St. Clair and Lake Ontario, respectively. Moreover, the violin plot of the deep learning model for each lake was most similar to the violin plot of the observed data. Hence, the deep learning model outperformed the ANN and ARIMA model in each lake. Full article

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12 pages, 1064 KiB

Open AccessArticle

Study of Volatile Organic Compounds in Emission from Bottom Sediments of Three Lakes with Impact of Anthropopression Using the Proton Transfer Reaction Mass Spectrometry

by Józef Antonowicz and Tomasz Wróblewski

Limnol. Rev. 2024, 24(3), 205-216; https://doi.org/10.3390/limnolrev24030012 - 6 Jul 2024

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Abstract

Studies of volatile organic compounds (VOCs) emitted from the bottom sediments of three Pomeranian lakes in Poland: Łazienkowskie, Rychnowskie, and Jeleń were conducted. All three lakes are subject to anthropogenic pressure but to varying degrees. In 2021, bottom sediment samples were taken from the lakes studied and an analysis of the emission of 20 volatile organic compounds was carried out using a proton transfer reaction mass spectrophotometer (PTR-MS). Concentrations in emissions from the bottom sediments of VOCs with the following mass–charge ratio (m/z) were analyzed: 57, 61, 63, 69, 75, 81, 83, 85, 87, 95, 97, 99, 101, 109, 111, 127, 129, 137, 149, and 157. The obtained data were analyzed by performing statistical tests and multivariate cluster and PCA analysis. The analysis shows that the lowest concentrations of VOCs were observed from bottom sediments in Lake Jeleń, which is subject to the lowest anthropopressure among the studied lakes. The analysis shows that the lowest concentrations of VOCs were observed from bottom sediments in Lake Jeleń, which is subject to lower anthropopressure among the studied lakes. With the help of cluster analysis, it was possible to collect data on the VOC concentrations into clusters, which resulted in demonstrating similarities between Łazienkowskie and Rychnowskie lakes—lakes connected by an isthmus, and the different characteristics of Lake Jeleń. PCA analysis leads to similar observations. The tested m/z VOCs can be identified using additional analytical methods. Full article

13 pages, 7761 KiB

Open AccessArticle

Numerical Simulation for the Desired Compatibility between the Inside Slopes of Open Irrigation Canals, and the Used Type of Wing Walls for the Most Efficient Performance of Water Structures

by Mohamed A. Ashour, Haitham M. Abueleyon, M. Khairy Ali, Abdallah A. Abdou and Tarek S. Abu-Zaid

Limnol. Rev. 2024, 24(3), 192-204; https://doi.org/10.3390/limnolrev24030011 - 28 Jun 2024

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Abstract

The design of water structures is crucial for efficient hydraulic performance. Open irrigation canals are designed with specific inside slopes to ensure maximum stability, while the wing walls of water structures constructed across the canal are designed to maximize hydraulic performance. Therefore, ensuring compatibility between the canal inside slopes and the wing wall types used on both the upstream and downstream sides is of great importance for achieving optimum hydraulic performance. However, our literature review indicates that this necessary compatibility between the canal inside slope and the wing wall type has not been adequately researched and studied. This present study aims to numerically investigate the relationship between open canals inside slopes and wing wall types, as well as examine the impact of using different wing wall types with varying canals inside slopes on hydraulic performance efficiency. Four canal inside slope ratios (Z) (H: V = 2:1, 1.5:1, 1:1, and 0.75:1) are simulated using the HEC-RAS program, along with two types of water structure wing walls (box and broken). The HEC-RAS numerical model provides accurate and reliable estimations of the hydraulic characteristics of flowing water through the structure, and the results are verified using previous experimental measurements available in the literature. The variation (ε%) between the measured and computed results is consistent for estimating specific energy, velocity, heading (afflux), and water depths. The simulation results demonstrate that changing the canal inside slope (Z) from 0.75:1 to 2:1 results in a relative increase of approximately 27.84% in heading up and 15.06% in velocity. Additionally, the broken wing wall proves to be more effective than the box type. The study confirms that the optimal configuration for the most efficient performance of water structures involves utilizing broken-type wing walls on the upstream side, along with a 1H:1V canal inside slope. This configuration reduces the relative velocity and relative heading by approximately 12% and 20%, respectively, which is considered highly favorable. Full article

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14 pages, 3585 KiB

Open AccessArticle

Quantification of Nitrate Level in Shallow and Deep Groundwater Wells for Drinking, Domestic and Agricultural Uses in Northeastern Arid Regions of Saudi Arabia

by Al Mamun and Hatim O. Sharif

Limnol. Rev. 2024, 24(3), 178-191; https://doi.org/10.3390/limnolrev24030010 - 24 Jun 2024

Viewed by 363

Abstract

Nitrate (NO₃⁻) is a vulnerable natural contaminant that can be found in groundwater. The estimated nitrate concentrations for four categories of wells in the northeastern arid regions of Saudi Arabia—commercial treated water stations for drinking, commercial stations of untreated water for domestic uses, private wells of residences for households, and private wells for agricultural uses—were found to be in the 16–380 mg/L range. Drinking water from all commercial treated water stations has lower nitrate levels, based on the WHO standard of 50 mg/L. In contrast, almost 33% of commercial stations with untreated water (used only for domestic purposes) in the studied areas had higher nitrate levels that were unsuitable for drinking. Approximately half of the private wells of residences and wells for agricultural uses had very high nitrate levels. They can be considered unsuitable for drinking due to excessive levels of nitrates but can be used for domestic and agricultural purposes. Thus, adopting specific strategies to reduce nitrate levels in public wells in the studied areas is crucial. The data obtained in the present study are essential for equipping decision-makers with valuable insights, allowing them to enact appropriate measures, as needed, and uphold community health in the studied regions. Full article

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Limnol. Rev., Volume 24, Issue 3 (September 2024) – 4 articles

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