共查询到20条相似文献，搜索用时 31 毫秒
1.
In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L?1) and Cl (meq L?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively. 相似文献
2.
Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model 
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下载免费PDF全文 Mohammad Taghi Sattari Rasoul Mirabbasi Reza Shamsi Sushab John Abraham 《Ground water》2018,56(4):636-646
The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively. 相似文献
3.
AbstractAccurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.
Editor D. Koutsoyiannis 相似文献
4.
Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin 总被引：3，自引：3，他引：0
Sarvin Zaman Zad Ghavidel Majid Montaseri 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(8):2101-2118
A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models. 相似文献
5.
ABSTRACTMonthly water balance models (MWBMs) are often used for making flow projections under climate change. As such, these models should provide accurate flow simulations; however, they are seldom evaluated in this regard. This paper presents a comprehensive framework intended for the evaluation of the applicability of MWBMs under changing climatic conditions. The framework consists of analyses of consistency in model performance, parameter estimates and simulated water balance components, and a subjective assessment of model transferability. Four MWBMs – abcd, Budyko, GR2M and WASMOD – are used to simulate runoff in the Wimmera catchment affected by the Millennium drought. Although abcd and Budyko slightly outperformed GR2M and WASMOD, none of the models performed well in transfer to the driest period. The greatest variability is detected in simulated groundwater storage and baseflow; thus, these model components should be improved and/or enhanced calibration strategies should be employed to advance the transferability of MWBMs under changing climate. 相似文献
6.
AbstractThe honey-bees mating programming (HBMP) algorithm is introduced as a novel tool for predicting suspended sediment concentration for the Mad River catchment near Arcata, USA. The paper also applies gene expression programming (GEP) as a comparison and shows that these two approaches can the produce transparent, nonlinear relationships between the independent and dependent variables. Some modifications have been made to the HBMP algorithm to improve its capability and efficiency. The results achieved from this method and GEP are compared with two different sediment rating curves based on regression techniques. The findings show that the results from both the HBMP and GEP methods are promising and outperform the results obtained from the sediment rating curves.
Editor D. Koutsoyiannis; Associate editor L. See 相似文献
7.
I. Touhami 《水文科学杂志》2013,58(1):193-203
AbstractEstimating groundwater recharge is essential to ensure the sustainable use of groundwater resources, particularly in arid and semi-arid regions. Soil water balances have been frequently advocated as valuable tools to estimate groundwater recharge. This article compares the performance of three soil water balance models (Hydrobal, Visual Balan v2.0 and Thornthwaite) in the Ventós-Castellar aquifer, Spain. The models were used to simulate wet and dry years. Recharge estimates were transformed into water table fluctuations by means of a lumped groundwater model. These, in turn, were calibrated against piezometric data. Overall, the Hydrobal model shows the best fit between observed and calculated levels (r2 = 0.84), highlighting the role of soil moisture and vegetation in recharge processes.Editor D. Koutsoyiannis; Associate editor X. ChenCitation Touhami, I., et al., 2014. Comparative performance of soil water balance models in computing semi-arid aquifer recharge. Hydrological Sciences Journal, 59 (1), 193–203. 相似文献
8.
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
9.
André Gustavo da Silva Melo Honorato Gustavo Barbosa Lima da Silva Celso Augusto Guimarães Santos 《水文科学杂志》2013,58(15-16):2060-2075
ABSTRACTCombinations of low-frequency components (also known as approximations) resulting from the wavelet decomposition are tested as inputs to an artificial neural network (ANN) in a hybrid approach, and compared to classical ANN models for flow forecasting for 1, 3, 6 and 12 months ahead. In addition, the inputs are rewritten in terms of the flow, revealing what type of information was being provided to the network, in order to understand the effect of the approximations on the forecasting performance. The results show that the hybrid approach improved the accuracy of all tested models, especially for 1, 3 and 6 months ahead. The input analyses show that high-frequency components are more important for shorter forecast horizons, while for longer horizons, they may worsen the model accuracy. 相似文献
10.
Tran Van Phong Binh Thai Pham Phan Trong Trinh Hai-Bang Ly Quoc Hung Vu Lanh Si Ho Hiep Van Le Lai Hop Phong Mohammadtaghi Avand Indra Prakash 《Ground water》2021,59(5):745-760
Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation. 相似文献
11.
Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater 下载免费PDF全文
下载免费PDF全文 Sasmita Sahoo 《水文研究》2015,29(5):671-691
Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r2 and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
12.
AbstractA comprehensive hydro-ecological investigation was conducted to determine the ecological response of increased groundwater withdrawals from the Kirkwood-Cohansey aquifer system, an important source of water supply in southern New Jersey, USA. Collocated observations were made of aquatic-macroinvertebrate assemblages and stream hydrologic attributes to develop flow–ecology response relations. A sub-regional transient groundwater flow model (MODFLOW) was used to simulate three plausible high-stress groundwater-withdrawal scenarios which resulted in stream baseflow reductions of approximately 0.12, 0.20, and 0.26 m3 s-1. These reduction scenarios were used to construct flow-alteration ecological response models to evaluate aquatic-macroinvertebrate response to streamflow reduction. For example, flow-alteration ecological response models indicate that if groundwater withdrawals diminish mean annual streamflow from 1.1 to 0.6 m3 s-1, the abundance of intolerant taxa could be reduced by as much as 20%. These flow-alteration ecological response modelling results could be used by resource professionals to evaluate alternative water management strategies to determine maximum basin withdrawal rates that meet ongoing human water demand while protecting biological integrity.
Editor D. Koutsoyiannis; Guest editor M. AcremanCitation Kennen, J.G., Riskin, M.L., and Charles, E.G., 2014. Effects of streamflow reductions on aquatic macroinvertebrates: linking groundwater withdrawals and assemblage response in southern New Jersey streams, USA. Hydrological Sciences Journal, 59 (3–4), 545–561. 相似文献
13.
Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins. 相似文献
14.
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
15.
Sergio E. Serrano 《水文科学杂志》2013,58(1):177-205
Abstract Mathematical models are the means to characterize variables quantitatively in many groundwater problems. Recent advances in applied mathematics have perfected what is now called Adomian's decomposition method (ADM), a simple modelling procedure for practical applications. Decomposition exhibits the benefits of analytical solutions (i.e. stability, analytic derivation of heads, gradients, fluxes and simple programming). It also offers the advantages of traditional numerical methods (i.e. consideration of heterogeneity, irregular domain shapes and multiple dimensions). In addition, decomposition is one of the few systematic procedures for solving nonlinear equations. By far its greatest advantage is its simplicity of application. It may produce simple results for preliminary simulations, or in cases with scarce information. The method is described with simple applications to regional groundwater flow. Many applications in groundwater flow and contaminant transport are available in the literature. Editor D. Koutsoyiannis; Associate editor Xi Chen Citation Serrano, S.E., 2013. A simple approach to groundwater modelling with decomposition. Hydrological Sciences Journal, 58 (1), 1–9. 相似文献
16.
Urban stormwater thermal gene expression models for protection of sensitive receiving streams 下载免费PDF全文
下载免费PDF全文 Thermal impact of typical high‐density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and cool‐water streams. This is the first study of its kind that provides simple easy‐to‐use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009–2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90–0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff. 相似文献
17.
Robert Reinecke Alexander Wachholz Steffen Mehl Laura Foglia Christoph Niemann Petra Döll 《Ground water》2020,58(3):363-376
Global-scale gradient-based groundwater models are a new endeavor for hydrologists who wish to improve global hydrological models (GHMs). In particular, the integration of such groundwater models into GHMs improves the simulation of water flows between surface water and groundwater and of capillary rise and thus evapotranspiration. Currently, these models are not able to simulate water table depth adequately over the entire globe. Unsatisfactory model performance compared to well observations suggests that a higher spatial resolution is required to better represent the high spatial variability of land surface and groundwater elevations. In this study, we use New Zealand as a testbed and analyze the impacts of spatial resolution on the results of global groundwater models. Steady-state hydraulic heads simulated by two versions of the global groundwater model G3M, at spatial resolutions of 5 arc-minutes (9 km) and 30 arc-seconds (900 m), are compared with observations from the Canterbury region. The output of three other groundwater models with different spatial resolutions is analyzed as well. Considering the spatial distribution of residuals, general patterns of unsatisfactory model performance remain at the higher resolutions, suggesting that an increase in model resolution alone does not fix problems such as the systematic overestimation of hydraulic head. We conclude that (1) a new understanding of how low-resolution global groundwater models can be evaluated is required, and (2) merely increasing the spatial resolution of global-scale groundwater models will not improve the simulation of the global freshwater system. 相似文献
18.
Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input
Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi-arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross-correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and Nash-Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance. 相似文献
19.
C.J.C. Blanco S.S.M. Santos M.C. Quintas M.V.A. Vinagre A.L.A. Mesquita 《水文科学杂志》2013,58(7):1423-1433
AbstractThe objective of this study is to analyse three rainfall–runoff hydrological models applied in two small catchments in the Amazon region to simulate flow duration curves (FDCs). The simple linear model (SLM) considers the rainfall–runoff process as an input–output time-invariant system. However, the rainfall–runoff process is nonlinear; thus, a modification is applied to the SLM based on the residual relationship between the simulated and observed discharges, generating the modified linear model (MLM). In the third model (SVM), the nonlinearity due to infiltration and evapotranspiration is incorporated into the system through the sigmoid variable gain factor. The performance criteria adopted were a distance metric (δ) and the Nash-Sutcliffe coefficient (R2) determined between simulated and observed flows. The good results of the models, mainly the MLM and SVM, showed that they could be applied to simulate FDCs in small catchments in the Amazon region.Editor D. Koutsoyiannis; Associate editor A. MontanariCitation Blanco, C.J.C., Santos, S.S.M., Quintas, M.C., Vinagre, M.V.A., and Mesquita, A.L.A., 2013. Contribution to hydrological modelling of small Amazonian catchments: application of rainfall–runoff models to simulate flow duration curves. Hydrological Sciences Journal, 58 (7), 1–11. 相似文献
20.
Evangelos Rozos 《水文科学杂志》2020,65(7):1084-1095
ABSTRACTDuring the last few decades, hydrological models have become very powerful, capable of spatially analysing the hydrological information and accurately representing the geomorphological characteristics of the studied area. However, one of the drawbacks of this heightened intricacy is the amount of time required to set up a hydrological model. In this study, a simple methodology that requires only a minimum set-up time is presented. This methodology employs linear regression to combine the outputs of simple hydrological models to simulate hydrological responses. Two kinds of simple hydrological models are employed. The first one represents the characteristics of the streamflow attributed to overland flow, and the second the characteristics of the streamflow attributed to interflow and baseflow. The methodology was tested in 4 case studies, and the results were encouraging. The best performance was achieved in the case study with data of fine time step with significant length. 相似文献