共查询到20条相似文献，搜索用时 31 毫秒
1.
ABSTRACTNowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).
Editor D. Koutsoyiannis; Associate editor F. Pappenberger 相似文献
2.
Parveen Sihag Vijay P. Singh Anastasia Angelaki Vinod Kumar Alireza Sepahvand Evangelia Golia 《水文科学杂志》2013,58(13):1647-1658
ABSTRACTInfiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area. 相似文献
3.
Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River,Iran 总被引：3，自引：2，他引：1
Rahim Barzegar Jan Adamowski Asghar Asghari Moghaddam 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(7):1797-1819
The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels of northwest Iran’s Aji-Chay River was assessed. The models were calibrated, validated and tested using different subsets of monthly records (October 1983 to September 2011) of individual solute (Ca2+, Mg2+, Na+, SO4 2? and Cl?) concentrations (input parameters, meq L?1), and electrical conductivity-based salinity levels (output parameter, µS cm?1), collected by the East Azarbaijan regional water authority. Based on the statistical criteria of coefficient of determination (R2), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (NSC) and threshold statistics (TS) the ANFIS model was found to outperform the ANN model. To develop coupled wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies, Symlet or Haar mother wavelets of different lengths (order), each implemented at three levels. To predict salinity input parameter series were used as input variables in different wavelet order/level-AI model combinations. Hybrid wavelet-ANFIS (R2 = 0.9967, NRMSE = 2.9 × 10?5 and NSC = 0.9951) and wavelet-ANN (R2 = 0.996, NRMSE = 3.77 × 10?5 and NSC = 0.9946) models implementing the db4 mother wavelet decomposition outperformed the ANFIS (R2 = 0.9954, NRMSE = 3.77 × 10?5 and NSC = 0.9914) and ANN (R2 = 0.9936, NRMSE = 3.99 × 10?5 and NSC = 0.9903) models. 相似文献
4.
Parameter identification and uncertainty analysis for simulating streamflow in a river basin of Eastern India 总被引：3，自引：0，他引：3 
下载免费PDF全文
下载免费PDF全文 Uncertainty is inherent in modelling studies. However, the quantification of uncertainties associated with a model is a challenging task, and hence, such studies are somewhat limited. As distributed or semi‐distributed hydrological models are being increasingly used these days to simulate hydrological processes, it is vital that these models should be equipped with robust calibration and uncertainty analysis techniques. The goal of the present study was to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for simulating streamflow in a river basin of Eastern India, and to evaluate the performance of salient optimization techniques in quantifying uncertainties. The SWAT model for the study basin was developed and calibrated using Parameter Solution (ParaSol), Sequential Uncertainty Fitting Algorithm (SUFI‐2) and Generalized Likelihood Uncertainty Estimation (GLUE) optimization techniques. The daily observed streamflow data from 1998 to 2003 were used for model calibration, and those for 2004–2005 were used for model validation. Modelling results indicated that all the three techniques invariably yield better results for the monthly time step than for the daily time step during both calibration and validation. The model performances for the daily streamflow simulation using ParaSol and SUFI‐2 during calibration are reasonably good with a Nash–Sutcliffe efficiency and mean absolute error (MAE) of 0.88 and 9.70 m3/s for ParaSol, and 0.86 and 10.07 m3/s for SUFI‐2, respectively. The simulation results of GLUE revealed that the model simulates daily streamflow during calibration with the highest accuracy in the case of GLUE (R2 = 0.88, MAE = 9.56 m3/s and root mean square error = 19.70 m3/s). The results of uncertainty analyses by SUFI‐2 and GLUE were compared in terms of parameter uncertainty. It was found that SUFI‐2 is capable of estimating uncertainties in complex hydrological models like SWAT, but it warrants sound knowledge of the parameters and their effects on the model output. On the other hand, GLUE predicts more reliable uncertainty ranges (R‐factor = 0.52 for daily calibration and 0.48 for validation) compared to SUFI‐2 (R‐factor = 0.59 for daily calibration and 0.55 for validation), though it is computationally demanding. Although both SUFI‐2 and GLUE appear to be promising techniques for the uncertainty analysis of modelling results, more and more studies in this direction are required under varying agro‐climatic conditions for assessing their generic capability. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
5.
Huijuan Cui Vijay P. Singh 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1545-1563
This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination (r 2) of 0.876, which is slightly higher in the Upper Mississippi (r 2 = 0.932) than in the Lower Mississippi (r 2 = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively. 相似文献
6.
Abdelghani Boudhar Gilles Boulet Lahoucine Hanich Jean Emmanuel Sicart Abdelghani Chehbouni 《水文科学杂志》2013,58(5):931-943
ABSTRACTIn this study, we used an energy balance model and two simple methods based on readily available data to identify the processes driving the point-scale energy and mass balance of the snowpack. Data were provided from an experimental site located at 3200 m. All models were evaluated by comparing observed and modelled snow water equivalents. Performances are variable from one season to the next and the energy balance model gives better results (mean of root mean square error, RMSE = 25 mm and r2 = 0.90) than the two simplified approaches (mean of RMSE = 54 mm and r2 = 0.70). There are significant amounts of snow sublimation but they are highly variable from season to season, depending on wind conditions (between 7 and 20% of the total). While the main source of energy for melting is net radiation, the amount of heat brought by sensible heat flux is significant for two of the most windy snow seasons.
Editor Z.W. Kundzewicz Associate editor not assigned 相似文献
7.
枯水期咸潮入侵已经严重威胁到了感潮河流区域供水安全.本文通过构建避咸蓄淡供水模型,耦合了咸度预测、河库联合供水调度和供水安全分析模块,为依赖感潮河流为水源地的区域供水安全管理提供了一种整体思路和决策方法.以面向粤港澳大湾区珠海东部及澳门的珠江三角洲磨刀门水道取供水为例,基于潮汐、径流和风等因子及咸度实测数据,较好地拟合了基于BP神经网络的咸度预测模型,及含氯度与超标时间的曲线函数,建立了上游来水和咸度超标时间之间的联系,得到了避咸蓄淡取水时机.咸度预测与当地河库联合供水调度相结合,得到了上游枯水期来水过程的当地区域供需平衡状况.枯水期不考虑水库调蓄的资源性缺水临界需水量为3.22亿m3,水库参与调蓄的工程性缺水临界需水量为3.75亿m3.通过供水安全分析模块,基于设定的风险阈值和临界阈值识别出了不同需水规模的上游来水临界流量特征.对于当地规划的需水规模4.23亿m3,期望上游枯水期临界流量均值约为3372 m3/s.整体上来说,需水规模越大,对上游来水期望的临界流量越大,但同时还与枯水期流量分布有关. 相似文献
8.
AbstractRemote sensing has become promising in providing temporal and spatial information on biogeodynamics in large and open freshwater bodies. In optically complex environments, such as in the Western Basin of Lake Erie (WBLE), the water contains multiple biogeochemical constituents or colour producing agents (CPAs), such as phytoplankton, suspended matter and dissolved organic carbon; identifying and analysing such in-water constituents is crucial for understanding and assessing many biogeochemical processes. For example, concentrations of chlorophyll-a and total suspended matter can be used as proxies to assess phytoplankton dynamics and particulate loading. However, quantitative estimation of their concentrations from satellite observations is complicated when working with mixed spectral signatures. Hyperspectral remote sensing is fast emerging as a key technology for advanced and improved understanding of optically complex waters. This study estimates concentrations of chlorophyll-a and total suspended matter (TSM) in the WBLE by applying the partial least squares (PLS) method to a full range (400–900 nm) of continuous narrow spectral bands. The PLS method models the covariance between hyperspectral bands and CPAs, and identifies the optimal bands that characterize most of the variance in the CPAs. This method avoids the curse of dimensionality and the effects of multi-collinearity, a challenge that is associated with new-generation hyperspectral satellite sensors. Validation parameters for the PLS-based models produced R2 of 0.84 for chlorophyll-a (RMSE = 1.18 μg/L), and R2 of 0.90 for TSM (RMSE = 1.26 mg/L), illustrating the potential of the PLS method for isolating and extracting absorption features characterizing the various CPAs in optically complex Case II type waters.
Editor Z.W. Kundzewicz Associate editor Not assigned 相似文献
9.
Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques 总被引：3，自引：0，他引：3
Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R2 = 0.97, ANFIS R2 = 0.92 and Marciano R2 = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data. 相似文献
10.
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
11.
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
12.
《水文科学杂志》2012,57(15):1824-1842
ABSTRACTIn this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ETo) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (Tmin, Tmax, RH, Us, Rs) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ETo at study stations. 相似文献
13.
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
14.
This paper develops and applies the minimum relative entropy (MRE) theory with spectral power as a random variable for streamflow forecasting. The MRE theory consists of (1) hypothesizing a prior probability distribution for the random variable, (2) determining the spectral power distribution, (3) extending the autocorrelation function, and (4) doing forecasting. The MRE theory was verified using streamflow data from the Mississippi River watershed. The exponential distribution was chosen as a prior probability in applying the MRE theory by evaluating the historical data of the Mississippi River. If no prior information is given, the MRE theory is equivalent to the Burg entropy (BE) theory. The spectral density obtained by the MRE theory led to higher resolution than did the BE theory. The MRE theory did not miss the largest peak at 1/12th frequency, which is the main periodicity of streamflow of the Mississippi River, but the BE theory sometimes did. The MRE theory was found to be capable of forecasting monthly streamflow with a lead time from 12 to 48 months. The coefficient of determination (r 2) between observed and forecasted stream flows was 0.912 for Upper Mississippi River and was 0.855 for Lower Mississippi River. Both MRE and BE theories were generally more reliable and had longer forecasting lead times than the autoregressive (AR) method. The forecasting lead time for MRE and BE could be as long as 48–60 months, while it was less than 48 months for the AR method. However, BE was comparable to MRE only when observations fitted the AR process well. The MRE theory provided more reliable forecasts than did the BE theory, and the advantage of using MRE is more significant for downstream flows with irregular flow patterns or where the periodicity information is limited. The reliability of monthly streamflow forecasting was the highest for MRE, followed by BE followed by AR. 相似文献
15.
Empirical prediction equations of the form W = aQb have been reported for rills and rivers, but not for ephemeral gullies. In this study six experimental data sets are used to establish a relationship between channel width (W, m) and flow discharge (Q, m3 s?1) for ephemeral gullies formed on cropland. The resulting regression equation (W = 2·51 Q0·412; R2 = 0·72; n = 67) predicts observed channel width reasonably well. Owing to logistic limitations related to the respective experimental set ups, only relatively small runoff discharges (i.e. Q < 0·02 m3s?1) were covered. Using field data, where measured ephemeral gully channel width was attributed to a calculated peak runoff discharge on sealed cropland, the application field of the regression equation was extended towards larger discharges (i.e. 5 × 10?4m3s?1 < Q < 0·1 m3s?1). Comparing W–Q relationships for concentrated flow channels revealed that the discharge exponent (b) varies from 0·3 for rills over 0·4 for gullies to 0·5 for rivers. This shift in b may be the result of: (i) differences in flow shear stress distribution over the wetted perimeter between rills, gullies and rivers, (ii) a decrease in probability of a channel formed in soil material with uniform erosion resistance from rills over gullies to rivers and (iii) a decrease in average surface slope from rills over gullies to rivers. The proposed W–Q equation for ephemeral gullies is valid for (sealed) cropland with no significant change in erosion resistance with depth. Two examples illustrate limitations of the W–Q approach. In a first example, vertical erosion is hindered by a frozen subsoil. The second example relates to a typical summer situation where the soil moisture profile of an agricultural field makes the top 0·02 m five times more erodible than the underlying soil material. For both cases observed W values are larger than those predicted by the established channel width equation for concentrated flow on cropland. For the frozen soils the equation W = 3·17 Q0·368 (R2 = 0·78; n = 617) was established, but for the summer soils no equation could be established. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
16.
Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent 下载免费PDF全文
下载免费PDF全文 Santosh Nepal Jie Chen David J. Penton Luis E. Neumann Hongxing Zheng Shahriar Wahid 《水文研究》2017,31(1):51-68
Snow and glacial melt processes are an important part of the Himalayan water balance. Correct quantification of melt runoff processes is necessary to understand the region's vulnerability to climate change. This paper describes in detail an application of conceptual GR4J hydrological model in the Tamor catchment in Eastern Nepal using typical elevation band and degree‐day factor approaches to model Himalayan snow and glacial melt processes. The model aims to provide a simple model that meets most water planning applications. The paper contributes a model conceptualization (GR4JSG) that enables coarse evaluation of modelled snow extents against remotely sensed Moderate Resolution Imaging Spectroradiometer snow extent. Novel aspects include the glacial store in GR4JSG and examination of how the parameters controlling snow and glacial stores correlate with existing parameters of GR4J. The model is calibrated using a Bayesian Monte Carlo Markov Chain method against observed streamflow for one glaciated catchment with reliable data. Evaluation of the modelled streamflow with observed streamflow gave Nash Sutcliffe Efficiency of 0.88 and Percent Bias of <4%. Comparison of the modelled snow extents with Moderate Resolution Imaging Spectroradiometer gave R2 of 0.46, with calibration against streamflow only. The contribution of melt runoff to total discharge from the catchment is 14–16% across different experiments. The model is highly sensitive to rainfall and temperature data, which suffer from known problems and biases, for example because of stations being located predominantly in valleys and at lower elevations. Testing of the model in other Himalayan catchments may reveal additional limitations. © 2016 The Authors. Hydrological Processes published by John Wiley & Sons Ltd. 相似文献
17.
The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution. 相似文献
18.
Fikret Inal 《洁净——土壤、空气、水》2010,38(10):897-908
Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 µg/m3, 11.15 µg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. 相似文献
19.
The calibration of marine 14C dates requires the incorporation of regionally specific marine reservoir offsets known as ΔR, essential for accurate and meaningful inter-archive comparisons. Revised, regional ΔR (‘ΔRR’) values for the Barents Sea are presented for molluscs and cetaceans for the two latest iterations of the marine calibration curve, based on previously published pre-bomb live-collected and radiocarbon-dated samples (‘ΔRL’; molluscs: n = 16; cetaceans: n = 18). Molluscan ΔRR, determined for four broad regional oceanographic settings, are: western Svalbard (including Bjørnøya), −61 ± 37 14C yrs (Marine20), 94 ± 38 14C yrs (Marine13); Franz Josef Land, −277 ± 57 14C yrs (Marine20), −122 ± 38 14C yrs (Marine13); Novaya Zemlya, −156 ± 73 14C yrs (Marine20), 0 ± 76 14C yrs (Marine13); northern Norway, −86 ± 39 14C yrs (Marine20), 74 ± 24 14C yrs (Marine13). Molluscan ΔRR values are considered applicable to other marine carbonate materials (e.g., foraminifera, ostracods). Cetacean ΔRR are determined for toothed (n = 10) and baleen (n = 8) whales, and a combined toothed-baleen group (n = 18): toothed, −161 ± 41 14C yrs (Marine20), 1 ± 41 14C yrs (Marine13); baleen, −158 ± 43 14C yrs (Marine20), 8 ± 41 14C yrs (Marine13); combined baleen-toothed whales, −160 ± 41 14C yrs (Marine20), 4 ± 49 14C yrs (Marine13). Where identification and separation of baleen and toothed whales is impossible the combined ΔRR term may be used. However, we explicitly discourage the application of existing cetacean ΔRR terms to other marine mammals. Our new ΔRR values are applicable for as long as those broad oceanographic conditions (circulation and ventilation) have persisted, i.e., through the Holocene. We recommend using the latest iteration of the marine calibration curve, Marine20, which seems to better capture the time-variant nature of R compared to Marine13. More ΔRL datapoints for both molluscs and cetaceans would improve the accuracy and precision of ΔRR. In the meantime, our new ΔR terms facilitate the calibration of marine 14C dates across the region, paving the way for meaningful and accurate late Quaternary histories and inter-regional comparisons. 相似文献
20.
Abstract Electromagnetic induction measurements (EM) were taken in a saline gypsiferous soil of the Saharan-climate Fatnassa oasis (Tunisia) to predict the electrical conductivity of saturated soil extract (ECe) and shallow groundwater properties (depth, Dgw, and electrical conductivity, ECgw) using various models. The soil profile was sampled at 0.2 m depth intervals to 1.2 m for physical and chemical analysis. The best input to predict the log-transformed soil salinity (lnECe) in surface (0–0.2 m) soil was the EMh/EMv ratio. For the 0–0.6 m soil depth interval, the performance of multiple linear regression (MLR) models to predict lnECe was weaker using data collected over various seasons and years (R a 2 = 0.66 and MSE = 0.083 dS m-1) as compared to those collected during the same period (R a 2 = 0.97, MSE = 0.007 dS m-1). For similar seasonal conditions, for the Dgw–EMv relationship, R 2 was 0.88 and the MSE was 0.02 m for Dgw prediction. For a validation subset, the R 2 was 0.85 and the MSE was 0.03 m. Soil salinity was predicted more accurately when groundwater properties were used instead of soil moisture with EM variables as input in the MLR. Editor D. Koutsoyiannis; Associate editor K. Heal Citation Bouksila, F., Persson, M., Bahri, A., and Berndtsson, R., 2012. Electromagnetic induction predictions of soil salinity and groundwater properties in a Tunisian Saharan oasis. Hydrological Sciences Journal, 57 (7), 1473–1486. 相似文献