☆ 4.6 Article

Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models

SUSTAINABILITY (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Interdisciplinary Applications

A novel medium- and long-term runoff combined forecasting model based on different lag periods

Ping Ai et al.

Summary: This paper proposes a novel combined forecasting model for medium- and long-term runoff based on different lag periods. The model selects input factors through time-delay correlation analysis and stepwise regression analysis, and integrates the results of three single models using extreme learning machine. Experimental results show that the proposed model significantly improves the accuracy of medium- and long-term runoff forecasting.

JOURNAL OF HYDROINFORMATICS (2022)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Abnormal warm sea-surface temperature in the Indian Ocean, active potential vorticity over the Tibetan Plateau, and severe flooding along the Yangtze River in summer 2020

Tingting Ma et al.

Summary: Excessive precipitation was observed in the Yangtze River Valley during the record-breaking Meiyu season in 2020. The activity of high potential vorticity (PV) systems during their eastward propagation over the eastern Tibetan Plateau (TP) is responsible for the above-normal rainfall in both western and eastern China. The formation of high-PV systems is closely related to the thermal contrast between the near-surface and lower atmosphere, and their arrival timing at the eastern flank of the TP plays an important role in subsequent development and precipitation downstream.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2022)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: application of Lake Michigan

Vahdettin Demir

Summary: This study investigates the accuracy of three different techniques, MARS, LSSVR, and M5-tree, for estimating monthly lake levels with the periodicity component. The study uses data from Lake Michigan and finds that MARS generally outperforms LSSVR and M5-tree, and including the periodicity component improves the accuracy of the models.

THEORETICAL AND APPLIED CLIMATOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Regional characteristics' impact on the performances of the gated recurrent unit on streamflow forecasting

Qianyang Wang et al.

Summary: In this study, the performance of GRU model for one, two, and three-day-ahead streamflow forecasting was examined in seven basins in different geographic regions in China. The results showed that the GRU model outperformed the benchmark machine learning models in most cases, especially for one-day-ahead forecasting. However, its accuracy decreased with the increasing lead time.

WATER SUPPLY (2022)

添加到收藏夹

Article Engineering, Civil

Directed graph deep neural network for multi-step daily streamflow forecasting

Yongqi Liu et al.

Summary: In this paper, a spatial deep learning model called directed graph deep neural network is proposed for multi-step streamflow forecasting, which can utilize hydrological and meteorological information from multiple sites. Experimental results demonstrate its superior performance compared to other neural network models, and the uncertainty estimation using hidden Markov regression shows its ability to handle forecasting uncertainty.

JOURNAL OF HYDROLOGY (2022)

添加到收藏夹

Article Engineering, Civil

Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?

Vahid Moosavi et al.

Summary: It was found that in daily runoff modeling, the hybrid wavelet-GMDH model with a complete dataset as input and a 20-year data length provides the highest accuracy. The order of importance and effect on runoff prediction accuracy among the mentioned factors is as follows: input dataset, data length, preprocessing, and model type. GMDH and SVR showed the best performance, while wavelet and EEMD signal processing methods had the highest impact on the data driven models.

JOURNAL OF HYDROLOGY (2022)

添加到收藏夹

Article Chemistry, Analytical

A Cascaded Adaptive Network-Based Fuzzy Inference System for Hydropower Forecasting

Namal Rathnayake et al.

Summary: This paper examines the impact of climate change on the Samanalawewa Reservoir Hydropower Project and utilizes the Cascaded ANFIS algorithm to forecast future power generation. The results show that the algorithm accurately predicts the relationship between power generation and rainfall.

SENSORS (2022)

添加到收藏夹

Article Engineering, Civil

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

Icen Yoosefdoost et al.

Summary: This study simulated input runoff to a dam reservoir in an arid region under changing climatic conditions using three data-mining algorithms and a conceptual model. The SVM model showed remarkable performance in runoff simulations compared to other models. The study also predicted a decrease in mean runoff input for the future period, highlighting the need for sustainable adaptation strategies.

WATER RESOURCES MANAGEMENT (2022)

添加到收藏夹

Article Water Resources

Spatiotemporal variations in water conservation function of the Tibetan Plateau under climate change based on InVEST model

Yunfei Wang et al.

Summary: This study analyzed the changing trend of water conservation function in the Tibetan Plateau and identified important water conservation areas. The results showed that water conservation capacity in the Tibetan Plateau has been increasing, influenced by factors such as precipitation and vegetation index.

JOURNAL OF HYDROLOGY-REGIONAL STUDIES (2022)

添加到收藏夹

Article Engineering, Civil

A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box-Cox Transformation for Monthly Runoff Time Series Prediction

Fangqin Zhang et al.

Summary: This paper proposes a new hybrid prediction model named VMD-BC-Elman, which combines variational mode decomposition and Box-Cox transformation with Elman neural network to address the nonstationarity and skewness issues in monthly runoff time series. The model is evaluated using data from hydrological stations in the Wei River Basin in China, and the results show that it outperforms other single models and hybrid models.

WATER RESOURCES MANAGEMENT (2022)

添加到收藏夹

Article Engineering, Civil

Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model

Wenxin Xu et al.

Summary: The size of the watershed and the length of the training period have an impact on the predictive performance of deep learning models for monthly streamflow prediction. A larger watershed area is more suitable for the deep learning model, and increasing the training period improves the predictive performance.

WATER RESOURCES MANAGEMENT (2022)

添加到收藏夹

Article Environmental Sciences

Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin

Yuanxin Ren et al.

Summary: This study investigates the influence of time lag and lead time on runoff prediction results using deep learning models at different time scales in the upper Yangtze River basin. Different models have varying optimal time lag settings, and as time lag increases, simulation accuracy stabilizes after a specific time lag. The shortest lead time length can be chosen for the best prediction results at different time scales.

WATER (2022)

添加到收藏夹

Article Environmental Sciences

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Aman Arora et al.

Summary: This study tested and compared novel advanced-machine learning algorithms in predicting flood susceptible areas in a low-altitude, sub-tropical floodplain environment in the Middle Ganga Plain, India. The ANFIS-GA model outperformed other models with the highest success rate and prediction rate, suggesting further study in similar environments for establishing a benchmark model with highest accuracy and sensitivity.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

添加到收藏夹

Article Water Resources

Uncertainties and risks in reservoir operations under changing hydroclimatic conditions

Ali Aljoda et al.

Summary: The study highlights the importance of understanding the changing hydrologic regime of the Feather River Basin and its impacts on water resources decision variables. By using the S-Y-R model, the risk of the designed reservoir under temporal variation and nonstationarity is quantified, while potential linkages between long-term variability in the Feather River Inflow and climate variations are explored using wavelet spectrum and coherence analysis. The results show substantial variations in the hydrologic regime over different timescales and suggest strong associations between streamflow and climate teleconnection patterns during critical periods.

JOURNAL OF WATER AND CLIMATE CHANGE (2021)

添加到收藏夹

Article Environmental Sciences

Linkage between anomalies of pre-summer thawing of frozen soil over the Tibetan Plateau and summer precipitation in East Asia

Yuheng Li et al.

Summary: This study reveals that the pre-summer thawing of frozen soil over the Tibetan Plateau has a significant impact on summer precipitation in East Asia, with the thawing thickness showing a positive relationship with precipitation. This impact is mediated through changes in land surface heating and atmospheric circulation, leading to changes in the distribution of precipitation.

ENVIRONMENTAL RESEARCH LETTERS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions

Zhong-kai Feng et al.

Summary: This study proposes a hybrid river flow forecasting method by integrating the novel cooperation search algorithm (CSA) into the learning process of artificial neural network (ANN). The experimental results show that the hybrid method based on ANN and CSA outperforms control models and yields superior forecasting results in different scenarios. The presented method demonstrates improvements in the efficiency and correlation values of the standard ANN method, indicating that the performance of artificial intelligence models in river flow time series forecasting can be effectively improved by metaheuristic algorithms.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Construction & Building Technology

Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

Wen-jing Niu et al.

Summary: Accurate runoff forecasting is crucial for ensuring sustainable utilization and management of water resources. Research indicates that support vector machine, Gaussian process regression, and extreme learning machine outperform artificial neural network and adaptive neural based fuzzy inference system in streamflow prediction, emphasizing the importance of selecting appropriate forecasting models based on reservoir characteristics.

SUSTAINABLE CITIES AND SOCIETY (2021)

添加到收藏夹

Article Engineering, Civil

A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction

Lizhi Tao et al.

Summary: In this study, a new multiscale long short-term memory model with attention mechanism (MLSTM-AM) is proposed to improve the accuracy of monthly precipitation forecasting. The performance of different models varies in different regions of the Yangtze River basin, with MLSTM-AM providing the best prediction performance.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Engineering, Civil

Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation

Hossein Tabari

Summary: The study compares the effects of BM and POT methods on future climate change impacts on global floods and extreme precipitation. Both methods agree on the direction of changes, but differ in magnitude, especially for more extreme events. The discrepancy between BM and POT results increases with event extremity and varies with season, showing significant differences in global land areas during winter and summer.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions

Zhen-Qiang Zhou et al.

Summary: The record strong Indian Ocean Dipole event in 2019 played a key role in the severe flooding of the Yangtze River in 2020 by deepening the thermocline and warming the Indian Ocean. This led to anomalous anticyclone in the lower troposphere over the Indo-Northwest Pacific region and intensified the upper-level westerly jet over East Asia, causing heavy summer rainfall in the Yangtze Basin.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

The Record-breaking Meiyu in 2020 and Associated Atmospheric Circulation and Tropical SST Anomalies

Yihui Ding et al.

Summary: The record-breaking meiyu season in the Yangtze-Huaihe River valley in 2020 was characterized by long duration, abundant precipitation, and frequent heavy rainstorm processes, closely associated with a significant quasi-biweekly oscillation in the East Asian monsoon circulation system.

ADVANCES IN ATMOSPHERIC SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An Attentive Survey of Attention Models

Sneha Chaudhari et al.

Summary: Attention models have become crucial in neural networks and have been extensively researched. This survey provides an overview of attention modeling developments, introduces a taxonomy, reviews various techniques, discusses applications, and suggests future research directions.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2021)

添加到收藏夹

Article Automation & Control Systems

A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System

Namal Rathnayake et al.

Summary: The ANFIS model is widely used for its smoothness and adaptability, but faces challenges related to dimensionality and computational complexity. A novel Cascaded ANFIS algorithm is proposed in this paper, utilizing pair selection and training modules to enhance performance and minimize errors in various datasets.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2021)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Tracking the Atmospheric-Terrestrial Water Cycle over the Tibetan Plateau Based on ERA5 and GRACE

Yonghui Lei et al.

Summary: This study provides a preliminary assessment of the atmospheric-terrestrial water cycle over the Tibetan Plateau and its drainage basins, showing that the net water fluxes are closely tied to local dynamics and atmospheric water vapor. ERA5 and GRACE datasets show good correspondence in the long- and short-term variations over the Tibetan Plateau.

JOURNAL OF CLIMATE (2021)

添加到收藏夹

Article Water Resources

Assessing rainwater harvesting potential in a humid and semi-humid region based on a hydrological model

Kaisheng Luo et al.

Summary: This study evaluated the rainwater harvesting potential in the upper regions of the Yangtze River in China during the dry season, providing insights into the structure and spatial distribution of the potential. It was found that the rainwater harvesting potential has a relatively stable inter-annual variation with a weak increasing trend, primarily regulated by rainfall. Overall, the rainwater harvesting potential can help alleviate agricultural drought in the region, offering a reference for evaluating potential in humid and semi-humid zones during dry seasons.

JOURNAL OF HYDROLOGY-REGIONAL STUDIES (2021)

添加到收藏夹

Article Engineering, Environmental

Prediction of runoff in the upper Yangtze River Based on CEEMDAN-NAR model

Xianqi Zhang et al.

Summary: This study evaluates the ability of the Nonlinear Auto Regressive (NAR) model in predicting runoff volume in the upper reaches of the Yangtze River. By using the CEEMDAN preprocessing technique, the coupled CEEMDAN-NAR model shows better predictive ability than the single model, with a deterministic coefficient (DC) of 0.93 and prediction accuracy reaching Class A.

WATER SUPPLY (2021)

添加到收藏夹

Article Engineering, Civil

Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence

Wen-jing Niu et al.

Summary: Accurate hydrologic forecasting is crucial for water resource planning and management. A hybrid hydrological forecasting method incorporating signal decomposition reconstruction and swarm intelligence has been developed, demonstrating improved prediction accuracy compared to traditional models. The method utilizes ensemble empirical mode decomposition and least-squares support vector machine to predict monthly runoff data from two hydrological stations in China.

JOURNAL OF HYDROLOGIC ENGINEERING (2021)

添加到收藏夹

Article Engineering, Civil

An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion

Pengyu Lu et al.

Summary: This study proposes a tailor-made method for ensemble forecasts of monthly estuarine saltwater intrusion by using a combination of Pearson's Coefficient and Maximal Information Coefficient to determine the initial set of candidates and reducing the dimension of the input data sets through Principal Component Analysis. The results indicated that the proposed method achieved the highest forecast accuracy with a Nash-Sutcliffe coefficient of 0.78.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Engineering, Civil

A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction

Mahdi Abbasi et al.

Summary: The study demonstrates that the DAE-SVR model provides significant improvement in runoff prediction accuracy and uncertainty reduction, outperforming other hybrid models in various forecast horizons.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Water Resources

Multiscale attribution analysis for assessing effects of changing environment on runoff: case study of the Upstream Yangtze River in China

Yu Zhang et al.

Summary: This study identified significant decreases in runoff in the Upstream Yangtze River, particularly in the mainstream and certain tributaries, attributed mainly to climate change.

JOURNAL OF WATER AND CLIMATE CHANGE (2021)

添加到收藏夹

Article Engineering, Civil

Climate change impacts on Three Gorges Reservoir impoundment and hydropower generation

Pengcheng Qin et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Energy & Fuels

A hybrid model for building energy consumption forecasting using long short term memory networks

Nivethitha Somu et al.

APPLIED ENERGY (2020)

添加到收藏夹

Article Engineering, Civil

Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization

Zhong-kai Feng et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Water Resources

Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods

Xiaofei Chen et al.

JOURNAL OF HYDROLOGY AND HYDROMECHANICS (2020)

添加到收藏夹

Article Environmental Sciences

Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse

Zening Wu et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting

Pa Ousman Bojang et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Development of advanced artificial intelligence models for daily rainfall prediction

Binh Thai Pham et al.

ATMOSPHERIC RESEARCH (2020)

添加到收藏夹

Review Engineering, Civil

A survey on river water quality modelling using artificial intelligence models: 2000-2020

Tiyasha et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Engineering, Civil

Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation

Shuai Gao et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Engineering, Civil

Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

Peiman Parisouj et al.

WATER RESOURCES MANAGEMENT (2020)

添加到收藏夹

Article Geosciences, Multidisciplinary

Enhanced Meiyu-Baiu Rainfall in Early Summer 2020: Aftermath of the 2019 Super IOD Event

Yuhei Takaya et al.

GEOPHYSICAL RESEARCH LETTERS (2020)

添加到收藏夹

Article Environmental Sciences

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Qianyang Wang et al.

WATER (2020)

添加到收藏夹

Article Engineering, Civil

A MCMC-based maximum entropy copula method for bivariate drought risk analysis of the Amu Darya River Basin

X. Yang et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Engineering, Civil

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Zaher Mundher Yaseen et al.

JOURNAL OF HYDROLOGY (2019)

添加到收藏夹

Article Engineering, Civil

Mechanisms influencing non-stationarity in rainfall-runoff relationships in southeast Australia

Proloy Deb et al.

JOURNAL OF HYDROLOGY (2019)

添加到收藏夹

Article Environmental Sciences

Runoff variation characteristics, association with large-scale circulation and dominant causes in the Heihe River Basin, Northwest China

Qingping Cheng et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Estimating the response of hydrological regimes to future projections of precipitation and temperature over the upper Yangtze River

Xiaoli Yang et al.

ATMOSPHERIC RESEARCH (2019)

添加到收藏夹

Article Engineering, Civil

A robust method for non-stationary streamflow prediction based on improved EMD-SVM model

Erhao Meng et al.

JOURNAL OF HYDROLOGY (2019)

添加到收藏夹

Article Geosciences, Multidisciplinary

Quasi-Biweekly Oscillation of the South Asian High and Its Role in Connecting the Indian and East Asian Summer Rainfalls

Wei Wei et al.

GEOPHYSICAL RESEARCH LETTERS (2019)

添加到收藏夹

Article Management

Deep learning with long short-term memory networks for financial market predictions

Thomas Fischer et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2018)

添加到收藏夹

Article Geography, Physical

Changes identification of the Three Gorges reservoir inflow and the driving factors quantification

Yu Zhang et al.

QUATERNARY INTERNATIONAL (2018)

添加到收藏夹

Article Engineering, Environmental

Monthly runoff forecasting based on LSTM-ALO model

Xiaohui Yuan et al.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2018)

添加到收藏夹

Review Environmental Sciences

Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi et al.

WATER (2018)

添加到收藏夹

Article Geosciences, Multidisciplinary

Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks

Frederik Kratzert et al.

HYDROLOGY AND EARTH SYSTEM SCIENCES (2018)

添加到收藏夹

Review Geosciences, Multidisciplinary

Copula-entropy theory for multivariate stochastic modeling in water engineering

Vijay P. Singh et al.

GEOSCIENCE LETTERS (2018)

添加到收藏夹

Review Green & Sustainable Science & Technology

A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

Zeyu Wang et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)

添加到收藏夹

Article Environmental Sciences

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Tiantian Yang et al.

WATER RESOURCES RESEARCH (2017)

添加到收藏夹

Article Engineering, Civil

Changes in monthly flows in the Yangtze River, China - With special reference to the Three Gorges Dam

Jing Chen et al.

JOURNAL OF HYDROLOGY (2016)

添加到收藏夹

Article Engineering, Multidisciplinary

Key Technologies of the Hydraulic Structures of the Three Gorges Project

Xinqiang Niu

ENGINEERING (2016)

添加到收藏夹

Review Physics, Multidisciplinary

Integrating Entropy and Copula Theories for Hydrologic Modeling and Analysis

Zengchao Hao et al.

ENTROPY (2015)

添加到收藏夹

Article Environmental Sciences

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition

Wen-chuan Wang et al.

ENVIRONMENTAL RESEARCH (2015)

添加到收藏夹

Article Meteorology & Atmospheric Sciences

Entropy-Copula in Hydrology and Climatology

Amir AghaKouchak

JOURNAL OF HYDROMETEOROLOGY (2014)

添加到收藏夹

Article Physics, Multidisciplinary

A new correlation coefficient for bivariate time-series data

Orhan Erdem et al.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2014)

添加到收藏夹

Article Computer Science, Information Systems

Mutual information is copula entropy

Jian Ma et al.

TSINGHUA SCIENCE AND TECHNOLOGY (2011)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Non-linear variable selection for artificial neural networks using partial mutual information

Robert J. May et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2008)

添加到收藏夹

Article Water Resources

Trends in suspended sediment grain size in the upper Yangtze River and its tributaries, as influenced by human activities

Jiongxin Xu

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2007)

添加到收藏夹

Article Business

Application of grey model GM(1,1) to vehicle fatality risk estimation

Mingzhi Mao et al.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2006)

添加到收藏夹

Article Water Resources

Trend test and change-point detection for the annual discharge series of the Yangtze River at the Yichang hydrological station

LH Xiong et al.

HYDROLOGICAL SCIENCES JOURNAL (2004)

添加到收藏夹

Article Environmental Sciences

Performance of partial Mann-Kendall tests for trend detection in the presence of covariates

C Libiseller et al.

ENVIRONMETRICS (2002)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

添加到收藏夹

Article Engineering, Civil

Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 2 - Predictor identification of quarterly rainfall using ocean-atmosphere information

A Sharma et al.

JOURNAL OF HYDROLOGY (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.