Prediction of hydraulics performance in drain envelopes using Kmeans based multivariate adaptive regression spline

Adnan, Rana Muhammad; Khosravinia, Payam; Karimi, Bakhtiar; Kişi, Özgür

doi:10.1016/j.asoc.2020.107008

Cited by 38 publications

(13 citation statements)

References 49 publications

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“…The process is iterated until the error is below the threshold. The choice of initial seed and the numbers of clusters are crucial, as they decide the KM method's accuracy [58]. The KM algorithm classifies the objects based upon characteristics into K number of groups.…”

Section: K-means (Km) Algorithm and Mars Hybrid Modelmentioning

confidence: 99%

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

et al. 2021

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The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

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Section: K-means (Km) Algorithm and Mars Hybrid Modelmentioning

confidence: 99%

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

et al. 2021

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“…Researchers worldwide use data-driven models for various applications, such as decoding clinical biomarker space of COVID-19 [17], water quality prediction [18], and pipe-break rate prediction [19]. Researchers attempt to use data-driven models for various hydrological predictions, such as MARS [20][21][22][23][24], DENFIS [25], LSTM-ALO [26], and LSSVR-GSA [27]. MARS uses forward and backward step to add and remove piecewise linear functions to fit the model.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is performance degradation if data contain too many variables. To get the best result, MARS requires variable selection [20][21][22][23][24]. DENFIS requires prior assumptions about data and needs domain knowledge to set predefined parameters.…”

Section: Introductionmentioning

confidence: 99%

Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

Lee

Kim

2021

Water

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The Soyang Dam, the largest multipurpose dam in Korea, faces water resource management challenges due to global warming. Global warming increases the duration and frequency of days with high temperatures and extreme precipitation events. Therefore, it is crucial to accurately predict the inflow rate for water resource management because it helps plan for flood, drought, and power generation in the Seoul metropolitan area. However, the lack of hydrological data for the Soyang River Dam causes a physical-based model to predict the inflow rate inaccurately. This study uses nearly 15 years of meteorological, dam, and weather warning data to overcome the lack of hydrological data and predict the inflow rate over two days. In addition, a sequence-to-sequence (Seq2Seq) mechanism combined with a bidirectional long short-term memory (LSTM) is developed to predict the inflow rate. The proposed model exhibits state-of-the-art prediction accuracy with root mean square error (RMSE) of 44.17 m3/s and 58.59 m3/s, mean absolute error (MAE) of 14.94 m3/s and 17.11 m3/s, and Nash–Sutcliffe efficiency (NSE) of 0.96 and 0.94, for forecasting first and second day, respectively.

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“…As the carrier of information, data must accurately and reliably reflect the objective things in the real world (Murtagh and Pierre, 2014 ; Brzezińska and Horyń, 2020 ). How to extract effective information on a large number of data sets for data mining, in addition to effective data analysis technology, good data quality is the basic condition of various data mining (Adnan et al, 2020 ). In the era of big data, data quality is a key issue that restricts the development of the data industry (Zeng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

et al. 2021

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The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

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Prediction of hydraulics performance in drain envelopes using Kmeans based multivariate adaptive regression spline

Cited by 38 publications

References 49 publications

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

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