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1.
ABSTRACTUnderstanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow. 相似文献
2.
Impact of complexity on daily and multi-step forecasting of streamflow with chaotic,stochastic, and black-box models 总被引：1，自引：0，他引：1
Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models. 相似文献
3.
Abstract The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall. Editor Z.W. Kundzewicz Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41. 相似文献
4.
ABSTRACTThe potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (Tdew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting Tdew values. 相似文献
5.
ABSTRACT Streamflow modeling is essential to investigate processes in the hydrologic cycle and important for water resource management application. However, in-situ hydrologic data paucity, because of various factors such as economic, political, instrument malfunctioning, and poor spatial distribution, makes the modeling process challenging. To overcome this limitation, we introduced a satellite remote sensing-based machine learning approach – boosted regression tree (BRT) – that integrates spatial land surface and climate variables that describe the sub-units, and applied it in three variable size watersheds in the Upper Mississippi River Basin (UMRB), USA. The model simulation results were tested using an independent dataset and showed Nash–Sutcliffe efficiency values of 0.80, 0.76, and 0.69 for the UMRB, Illinois River Watershed, and Raccoon River Watershed, respectively. In addition, we compared the performance of the machine learning models with existing process-based modeling results. Overall performance is comparable with the process-based approaches, but with significantly less modeling effort and resources. 相似文献
6.
Ani Shabri 《水文科学杂志》2013,58(7):1275-1293
Abstract This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting. Editor D. Koutsoyiannis; Associate editor L. See Citation Shabri, A. and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275–1293. 相似文献
7.
Comparison of daily streamflow forecasts using extreme learning machines and the random forest method 总被引：1，自引：0，他引：1
《水文科学杂志》2012,57(15):1857-1866
ABSTRACTDaily streamflow forecasting is a challenging and essential task for water resource management. The main goal of this study was to compare the accuracy of five data-driven models: extreme learning machine (basic ELM), extreme learning machine with kernels (ELM-kernel), random forest (RF), back-propagation neural network (BPNN) and support vector machine (SVR). The results show that the ELM-kernel model provided a superior alternative to the other models, and the basic ELM model had the poorest performance. To further evaluate the predictive capacities of the five models, the estimations of low flow and high flow in the testing dataset were compared. The RF model was slightly superior to the other models in predicting the peak flows, and the ELM-kernel model showed the highest prediction precision of low flows. There was no single model that showed obvious advantages over the other models in this study. Therefore, further exploration is required for the hydrological forecasting problems. 相似文献
8.
ABSTRACT Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection. 相似文献
9.
Adjusting wavelet‐based multiresolution analysis boundary conditions for long‐term streamflow forecasting 
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下载免费PDF全文 We propose a novel technique for improving a long‐term multi‐step‐ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks‐based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
10.
Abstract A trial is made to explore the applicability of chaos analysis outside the commonly reported analysis of a single chaotic time series. Two cross-correlated streamflows, the Little River and the Reed Creek, Virginia, USA, are analysed with regard to the chaotic behaviour. Segments of missing data are assumed in one of the time series and estimated using the other complete time series. Linear regression and artificial neural network models are employed. Two experiments are conducted in the analysis: (a) fitting one global model and (b) fitting multiple local models. Each local model is in the direct vicinity of the missing data. A nonlinear noise reduction method is used to reduce the noise in both time series and the two experiments are repeated. It is found that using multiple local models to estimate the missing data is superior to fitting one global model with regard to the mean squared error and the mean relative error of the estimated values. This result is attributed to the chaotic behaviour of the streamflows under consideration. 相似文献
11.
Shuang-Ye Wu 《水文科学杂志》2013,58(8):1251-1263
Abstract This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow. Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263. 相似文献
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13.
Bacterial concentration (Escherichia coli) is used as the key indicator for marine beach water quality in Hong Kong. For beaches receiving streamflow from unsewered catchments, water quality is mainly affected by local nonpoint source pollution and is highly dependent on the bacterial load contributed from the catchment. As most of these catchments are ungauged, the bacterial load is generally unknown. In this study, streamflow and the associated bacterial load contributed from an unsewered catchment to a marine beach, Big Wave Bay, are simulated using a modelling approach. The physically based distributed hydrological model, MIKE‐SHE, and the empirical watershed water quality model (Hydrological Simulation Program – Fortran) are used to simulate streamflow and daily‐averaged E. coli concentration/load, respectively. The total daily derived loads predicted by the model during calibration (June–July 2007) and validation (July–October 2008) periods agree well with empirical validation data, with a percentage difference of 3 and 2%, respectively. The simulation results show a nonlinear relationship between E. coli load and rainfall/streamflow and reveal a source limiting nature of nonpoint source pollution. The derived load is further used as an independent variable in a multiple linear regression (MLR) model to predict daily beach water quality. When compared with the MLR models based solely on hydrometeorological input variables (e.g. rainfall and salinity), the new model based on bacterial load predicts much more realistic E. coli concentrations during rainstorms. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
14.
ABSTRACTThe potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error. 相似文献
15.
A p‐order multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of data‐driven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or Pearson III) and the form of dependence (linear or non‐linear). The p‐order multivariate kernel density model is a non‐parametric method for synthesis of streamflow. The model is more flexible than conventional parametric models used in stochastic hydrology. The effectiveness and satisfactoriness of this model are illustrated through its application to the simultaneous synthetic generation of daily streamflow from Pingshan station and Yibin‐Pingshan region (Yi‐Ping region) of the Jinsha River in China. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
16.
Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities,data uncertainties,and multivariate dependencies. Part II: Application to streamflow simulation in the Xingshan Watershed,China 下载免费PDF全文
下载免费PDF全文 Provision of reliable scientific support to socio‐economic development and eco‐environmental conservation is challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies of hydrological systems in the Three Gorges Reservoir (TGR) region, China. Among them, the irregular nonlinearities mainly represent unreliability of regular functions for robust simulation of highly complicated relationships between variables. Based on the proposed discrete principal‐monotonicity inference (DPMI) approach, streamflow generation in the Xingshan Watershed, a representative watershed in this region, is examined. Based on system characterization, predictor identification, and streamflow distribution transformation, DPMI parameters are calibrated through a two‐stage strategy. Results indicate that the modelling efficiency of DPMI is satisfactory for streamflow simulation under these complexities. The distribution transformation method and the two‐stage calibration strategy can deal with non‐normality of streamflow and temporally unstable accuracy of hydrological models, respectively. The DPMI process and results reveal that both streamflow uncertainty and its rising tendency increase with flow levels. The dominant driving forces of streamflow generation are daily lowest temperature and daily cumulative precipitation in consideration of performances in global and local scales. The temporal heterogeneity of local significances to streamflow is insignificant for meteorological conditions. There is significant nonlinearity between meteorological conditions and streamflow and dependencies among meteorological conditions. The generation mechanism of low flows is more complicated than medium flows and high flows. The DPMI approach can facilitate improving robustness of hydro‐system analysis studies in the Xingshan Watershed or the TGR region. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
17.
《水文科学杂志》2013,58(3):640-655
Abstract Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables. 相似文献
18.
A.T. Silva 《水文科学杂志》2013,58(5):942-955
Abstract The method of fragments is applied to the generation of synthetic monthly streamflow series using streamflow data from 34 gauging stations in mainland Portugal. A generation model based on the random sampling of the log-Pearson Type III distribution was applied to each sample to generate 1200 synthetic series of annual streamflow with an equal length to that of the sample. The synthetic annual streamflow series were then disaggregated into monthly streamflows using the method of fragments, by three approaches that differed in terms of the establishment of classes and the selection of fragments. The results of the application of such approaches were compared in terms of the capacity of the method to preserve the main monthly statistical parameters of the historical samples. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Silva, A.T. and Portela, M.M., 2012. Disaggregation modelling of monthly streamflows using a new approach of the method of fragments. Hydrological Sciences Journal, 57 (5), 942–955. 相似文献
19.
AbstractNew wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. Editor D. Koutsoyiannis; Associate editor L. SeeCitation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324. 相似文献
20.
Brenton A. Wilder Jeremy T. Lancaster Peter H. Cafferata Drew B. R. Coe Brian J. Swanson Donald N. Lindsay William R. Short Alicia M. Kinoshita 《水文研究》2021,35(1):e13976
Following wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post-fire peak streamflow. We re-evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre-fire peak streamflow prediction performance was low, where the average R2 was 0.29 and average RMSE was 1.10 cms/km2 for the 2- and 10-year recurrence interval events, respectively. Post-fire, RCS performance was also low, with an average R2 of 0.26 and RMSE of 15.77 cms/km2 for the 2- and 10-year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF-45 and RF-5, respectively) to predict post-fire peak streamflow. RF-45 and RF-5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three-dimensional polynomial function to calculate post-fire peak streamflow in small catchments in southern California during the first year after fire (R2 = 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post-fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post-fire flood risk. 相似文献