Reservoir water quality simulation with data mining models

Arefinia, Ali; Bozorg‐Haddad, Omid; Oliazadeh, Arman; Loáiciga, Hugo A.

doi:10.1007/s10661-020-08454-4

Cited by 21 publications

(6 citation statements)

References 30 publications

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“…The emergence of distributed computing provides a new direction for processing and analyzing massive amounts of data. Distributed computing means that many software share information [ 20 ]. This software can run either on the same computer or on multiple computers connected through a network.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, continuous attribute discretization is also one of the main focuses of scholars' research at this stage. There are many different types of discretization algorithms, each with its own characteristics, but the core of the discretization algorithm is the same; that is, the selection of the best cutting and merging points is the key in the process of discretization operation, which is also the difficulty and focus of the research of discretization technology at this stage [ 20 ]. When preprocessing data, the most suitable discretization method should be selected according to the characteristics of data existence.…”

Section: Basic Principles and Concepts Of Algorithmmentioning

confidence: 99%

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Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

Zhou

2022

Computational Intelligence and Neuroscience

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With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy.

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Section: Related Workmentioning

confidence: 99%

Section: Basic Principles and Concepts Of Algorithmmentioning

confidence: 99%

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

Zhou

2022

Computational Intelligence and Neuroscience

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“…The logistic regression model needs to determine the effect of each variable based on the OR value of the independent variables in the multivariate analysis, so its prediction efficiency is relatively low [33]. ANN model has strong learning ability and fault tolerance ability, and its ability to identify and predict new samples is outstanding, and it is better than the logistic regression model [34]. Using 10 Journal of Sensors the PSO algorithm to improve FCM can improve the global search ability of the algorithm, reduce the scale difference between data objects, avoid the FCM algorithm falling into local extreme values, and reduce the sensitivity of the algorithm to the initial value setting [35].…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer

Yang

Qiu

et al. 2022

Journal of Sensors

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This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C -means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly ( P < 0.05 ). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher ( P < 0.05 ). The survival time of high-risk EC patients was much shorter than that of low-risk patients ( P < 0.05 ). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients ( P < 0.05 ). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.

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“…ANN, developed by McCollock and Walterpits 55 , is an artificial intelligence-based computational method that features an information processing system that employs interconnected data structures to emulate information processing by the human brain 56 . A neural network does not require precise mathematical algorithms and, like humans, can learn through input/output analysis relying on explicit instructions 57 . A simple neural network contains one input layer, one hidden layer, and one output layer.…”

Section: Methodsmentioning

confidence: 99%

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Zarei

Bozorg‐Haddad

Baghban

et al. 2021

Sci Rep

Self Cite

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Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

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Reservoir water quality simulation with data mining models

Cited by 21 publications

References 30 publications

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model

Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

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