Water

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16 pages, 4069 KiB

Open AccessArticle

The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods

by Mirac Nur Ciner, Mustafa Güler, Ersin Namlı, Mesut Samastı, Mesut Ulu, İsmail Bilal Peker and Sezar Gülbaz

Water 2024, 16(8), 1125; https://doi.org/10.3390/w16081125 - 15 Apr 2024

Viewed by 564

Abstract

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability [...] Read more.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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19 pages, 7118 KiB

Open AccessArticle

A Method for Identifying Gross Errors in Dam Monitoring Data

by Liqiu Chen, Chongshi Gu, Sen Zheng and Yanbo Wang

Water 2024, 16(7), 978; https://doi.org/10.3390/w16070978 - 28 Mar 2024

Viewed by 558

Abstract

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for [...] Read more.

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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19 pages, 3401 KiB

Open AccessArticle

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

by Zihui Huang, Chongshi Gu, Jianhe Peng, Yan Wu, Hao Gu, Chenfei Shao, Sen Zheng and Mingyuan Zhu

Water 2024, 16(2), 191; https://doi.org/10.3390/w16020191 - 05 Jan 2024

Cited by 1 | Viewed by 931

Abstract

The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and [...] Read more.

The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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18 pages, 5242 KiB

Open AccessArticle

Research on Temperature Control Index for High Concrete Dams Based on Information Entropy and Cloud Model from the View of Spatial Field

by Guang Yang, Jin Sun, Jianwei Zhang, Jingtai Niu, Bowen Luan, Zhendong Huang and Ahui Zhao

Water 2023, 15(22), 4023; https://doi.org/10.3390/w15224023 - 20 Nov 2023

Cited by 1 | Viewed by 777

Abstract

It is significant to adopt scientific temperature control criteria for high concrete dams in the construction period according to practical experience and theoretical calculation. This work synthetically uses information entropy and a cloud model and develops novel in situ observation data-based temperature control [...] Read more.

It is significant to adopt scientific temperature control criteria for high concrete dams in the construction period according to practical experience and theoretical calculation. This work synthetically uses information entropy and a cloud model and develops novel in situ observation data-based temperature control indexes from the view of a spatial field. The order degree and the disorder degree of observation values are defined according to the probability principle. Information entropy and weight parameters are combined to describe the distribution characteristics of the temperature field. Weight parameters are optimized via projection pursuit analysis (PPA), and then temperature field entropy (TFE) is constructed. Based on the above work, multi-level temperature control indexes are set up via a cloud model. Finally, a case study is conducted to verify the performance of the proposed method. According to the calculation results, the change law of TFEs agrees with actual situations, indicating that the established TFE is reasonable, the application conditions of the cloud model are wider than those of the typical small probability method, and the determined temperature control indexes improve the safety management level of high concrete dams. Research results offer scientific reference and technical support for temperature control standards adopted at other similar projects. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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16 pages, 3464 KiB

Open AccessArticle

The Use of UAV for Measuring the Morphology of Ice Cover on the Surface of a River: A Case Study of the Low Head Dam and Fishway Inlet Area in the Odra River

by Jan Błotnicki, Paweł Jarzembowski, Maciej Gruszczyński and Marcin Popczyk

Water 2023, 15(22), 3972; https://doi.org/10.3390/w15223972 - 15 Nov 2023

Cited by 1 | Viewed by 827

Abstract

The application of UAV to acquire data on the morphometry of frazil ice floe in motion is demonstrated in the measurements conducted in the area of the Wrocław Water Junction at the Opatowice weir on the Odra River (Poland). Image processing was performed [...] Read more.

The application of UAV to acquire data on the morphometry of frazil ice floe in motion is demonstrated in the measurements conducted in the area of the Wrocław Water Junction at the Opatowice weir on the Odra River (Poland). Image processing was performed using open-source software dedicated to image analysis. The methodology presented in the publication offers a cost-effective and low-overhead technique for describing ice phenomena in lowland rivers. The focus of the methodology was on measuring the area, average size, perimeter, and circularity of frazil ice floe. The measurements were carried out for individual frames captured by a UAV, and the results were analyzed using statistical techniques. In prior research, the team effectively assessed ice velocity on an identical test sample. Deriving the average velocity, surface area, and fundamental morphological traits of frazil ice facilitates the automated segmentation, classification, and prediction of potential risks related to ice blockages on water routes. These risks encompass potential waterway obstructions, as well as infrastructure impairments, and may pose a danger to human safety. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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20 pages, 5706 KiB

Open AccessArticle

Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

by Jianhe Peng, Wei Xie, Yan Wu, Xiaoran Sun, Chunlin Zhang, Hao Gu, Mingyuan Zhu and Sen Zheng

Water 2023, 15(21), 3724; https://doi.org/10.3390/w15213724 - 25 Oct 2023

Cited by 1 | Viewed by 869

Abstract

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features [...] Read more.

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R² and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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18 pages, 10230 KiB

Open AccessArticle

An Improved ResNet-Based Algorithm for Crack Detection of Concrete Dams Using Dynamic Knowledge Distillation

by Jingying Zhang and Tengfei Bao

Water 2023, 15(15), 2839; https://doi.org/10.3390/w15152839 - 06 Aug 2023

Viewed by 1260

Abstract

Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for users to deploy for real-time [...] Read more.

Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for users to deploy for real-time detection. However, compressing models face the dilemma of sacrificing detection accuracy. To solve this dilemma, an improved residual neural network (ResNet)-based algorithm for concrete dam crack detection using dynamic knowledge distillation is proposed in this paper in order to obtain higher accuracy for small models. To see how well distillation works, preliminary experiments were carried out on mini-ImageNet. ResNet18 was trained by adding additional tasks to match soft targets generated by ResNet50 under dynamic high temperatures. Furthermore, these pre-trained teacher and student models were transferred to experiments on concrete crack detection. The results showed that the accuracy of the improved algorithm was up to 99.85%, an increase of 4.92%. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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16 pages, 5051 KiB

Open AccessArticle

Stability Study of a Double-Row Steel Sheet Pile Cofferdam Structure on Soft Ground

by Yan Jiang, Fei Guo, Wenlong Wang, Guanghua Yang, Jinchao Yue and Yibin Huang

Water 2023, 15(14), 2643; https://doi.org/10.3390/w15142643 - 20 Jul 2023

Viewed by 3673

Abstract

The stability of a double-row steel sheet pile cofferdam structure under soft ground conditions was investigated in this study, using the temporary cofferdam of the Shenzhen–Zhongshan cross-river channel as the engineering background. The stability of the cofferdam design solution was calculated with a [...] Read more.

The stability of a double-row steel sheet pile cofferdam structure under soft ground conditions was investigated in this study, using the temporary cofferdam of the Shenzhen–Zhongshan cross-river channel as the engineering background. The stability of the cofferdam design solution was calculated with a model that incorporates factors such as the coordination of independent pile top displacement, as well as the m-value for backfilled sand and the thrown rock body. The internal force and displacement results of the cofferdam under different working conditions are obtained. And the entire construction process was analyzed using the finite element method. The results indicate that the overall stability and overturning stability of the cofferdam satisfy relevant safety requirements, with minimum safety factors of 1.744 and 1.400, respectively. The maximum displacement of the inner and outer steel sheet piles is 34 mm, the maximum bending moment is 249.30 kN·m, and the maximum shear force is 266.66 kN. The displacements of sheet piles were within an acceptable range, and the internal forces remained below the load capacity of the selected sheet pile type for the design. Based on these findings, the cofferdam structure can be considered safe and satisfying the specified requirements. This work may have instructive value for cofferdam design and construction. Full article

(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

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