Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Yoosefdoost, Icen; Khashei-Siuki, Abbas; Tabari, Hossein; Mohammadrezapour, Omolbani

doi:10.1007/s11269-022-03068-6

Cited by 26 publications

(6 citation statements)

References 45 publications

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“…To address this issue, we have developed a damage-resistant recovery mechanism to maintain a backup when the data are loaded to ensure its ability to withstand data errors. In this way, unconfirmed data can be retrieved from the backup for analysis and statistical correction after shutdown, even if abnormalities occur during system operation [18,19].…”

Section: Methodsmentioning

confidence: 99%

Cognitive Computing Model Based on Machine Learning Algorithm in Artificial Intelligence Environment

Liu

2022

Security and Communication Networks

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In order to effectively solve problems and complete data processing quickly, a data processing model based on computational knowledge training technology is prepared. First, prepare the cognitive model and then learn the data as techniques (distribution, decision-making, etc.) that can be used in the model and the process of scheduling work in the environment. The main points and progress of the research are as follows. Many people see the purpose of the problem that the data and data streams are difficult to identify and influence. The flow of knowledge data in relation to the use of the Internet is based on content-based counting patterns. It is to learn to correctly analyze the information points. In addition, in view of the problem of unsatisfactory effect caused by the randomness of the optional splitting behavior in the VFDT (Fast Decision Tree) algorithm, the decision tree algorithm for data flow is planned for the distribution of data flow. The accuracy of the algorithm can be close to 96%. The performance of the experiments was evaluated.

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Section: Methodsmentioning

confidence: 99%

Cognitive Computing Model Based on Machine Learning Algorithm in Artificial Intelligence Environment

Liu

2022

Security and Communication Networks

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“…Every DM algorithm always had some positive and negative properties when moving to a specific area. Data mining algorithms can improve the accuracy, understandability, interpretability, and stability of the generated results [9]. Alagrash et al designed a new genetic programming system by using a data mining algorithm and analyzed the calculation results of the automatic design algorithm.…”

Section: Introductionmentioning

confidence: 99%

Construction of German Machine Intelligent Translation System Based on Data Mining Algorithm

Liu

2022

International Transactions on Electrical Energy Systems

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The continuous expansion of the international market makes more people pay attention to learning different languages. As one of the most popular languages, German is favored by scholars. The use of intelligent translation systems to learn German has become the main way of learning. The increasing development of science and technology is accompanied by the emergence of a large number of new German words. The traditional German intelligent translation system has a slow update speed of words and cannot recognize new words, which seriously affects the accuracy of the translation. According to these questions, in this paper, the data mining algorithm is introduced into the German machine intelligent translation system. By evaluating the German machine intelligent translation system using a data mining algorithm in four aspects: translation speed, accuracy rate, recall rate, and user satisfaction, the evaluation results found that the translation speed of the system increased by 4.68%, the accuracy rate increased by 4.2, the recall rate has increased by 3.8%, and the user satisfaction score has increased by 0.825 points. The data mining algorithm can improve the German machine intelligent translation system and users’ interest in learning and promote German education.

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“…In light of the nonstationary attributes, including the dynamic linkages between the predictor and predictand within hydrological variables, it is crucial to devise a sophisticated algorithm. This algorithm should not only learn from but also adapt to the temporally evolving terrestrial environment and climatic conditions [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity

Zhang,

Xu,

Liu

et al. 2024

Water

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In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through monthly streamflow forecasting in the Kashgar River Basin of China, our method illuminates the temporal evolution of network relationships, underscoring the dynamism inherent in both input variables and modeling parameters. The key to our approach is identifying the most suitable time horizon (MST) for model updates, which is intricately problem-specific and crucial for peak performance. This methodology not only unveils changing predictor significance across varying flow conditions but also elucidates the fluctuating temporal links between variables, especially under the lens of climate change, for instance, the growing impact of snowmelt on the Kashgar Basin’s streamflow. Compared to stationary counterparts, our nonstationary Bayesian framework excels in capturing extreme events by adeptly accommodating temporal shifts, outperforming traditional models including both stationary and nonstationary variants of Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).

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Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Cited by 26 publications

References 45 publications

Cognitive Computing Model Based on Machine Learning Algorithm in Artificial Intelligence Environment

Cognitive Computing Model Based on Machine Learning Algorithm in Artificial Intelligence Environment

Construction of German Machine Intelligent Translation System Based on Data Mining Algorithm

Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity

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