1,720,982 research outputs found
Forecasting Reference Evapotranspiration Using Non-Linear Autoregressive Artificial Neural Network
Full text linkThe accurate forecast of reference evapotranspiration (ETo) has a vital role in real-time decisions on water resources management by quantifying the prospective changes in agricultural and hydrological processes. The real-time decisions on irrigation scheduling are primarily made based on the agricultural water demand predictions, which themselves strongly depend on ETo. Reference evapotranspiration is a complex process driven mainly by weather variables, and thus is characterized by high non-linearity and non-stationarity. In this study, the nonlinear autoregressive (NAR) and hybrid wavelet-NAR (WNAR) neural network approaches are used to forecast ETo for 1-, 3- and 7-days-ahead at six sites (namely, Alliance, Champion, Dunning, McCook, Mead, and North Platte) in Nebraska. These sites are chosen to cover various climatic conditions. At each site, 70%, 15%, and 15% of the daily ETo measurements from 1994 to 2015 are used respectively to train, validate, and test the NAR and WNAR networks. Thereafter, the trained NAR and WNAR networks are utilized to forecast ETo in 2016. The training and transfer functions as well as the number of feedback delays, hidden layers and nodes are determined by trial-and-error to optimize performance of the networks. Three training functions (i.e., Levenberg-Marquardt backpropagation (trainlm), resilient backpropagation (trainrp), and scaled conjugate gradient (trainscg)) andthree transfer functions (i.e., Log-Sigmoid (logsig), Tan-Sigmoid (tansig), and Radial Basis (radbas)) are utilized in the networks. It is found that the trainlm training function, tansig transfer function, 15 feedback delays, and 2 hidden layers with 20 nodes in each layer generate the best results. The findings show that the NAR and WNAR approaches can accurately forecast ETo. The six-site average mean absolute error (MAE) of 1-day-ahead ETo forecasts from NAR is 0.37 mm/day. The WNAR approach decreases the corresponding MAE to 0.23 mm/day. WNAR also improves the average root mean square error (RMSE), and the average coefficient of correlation (R2) from 0.73 mm/day to 0.54 mm/day, and from 0.94 to 0.96, respectively. The six-site average MAE of 3-day-ahead ETo forecasts from NAR is 0.78 mm/day. The WNAR approach decreases the corresponding MAE to 0.47 mm/day. WNAR also improves the RMSE, and R2 from 1.09 mm/day to 0.75 mm/day, and from 0.87 to 0.92, respectively. The six-site average MAE of 7-day-ahead ETo forecasts from NAR is 1.16 mm/day. The WNAR approach decreases the corresponding MAE to 0.91 mm/day. WNAR also improves the average RMSE and R2 from 1.48 mm/day to 1.24 mm/day, and from 0.74 to 0.78, respectively. Thus, the results support an overall better accuracy of the WNAR model when compared to the NAR model. Finally, performance of NAR is compared to those of autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA).M.S
Estimation Of Turbulent Heat Fluxes Via The Synergistic Assimilation Of Land Surface Temperature, Air Temperature And Specific Humidity Into A Variational Data Assimilation Model
Full text linkThe balance of energy at the Earth's surface is linked to the overlying atmospheric boundary layer (ABL). The sensible (H) and latent (LE) heat fluxes are important components of Earth’s radiation budget and its climate system, which directly influence the properties of the boundary layer and characterize exchange of heat and moisture between the land surface and its overlying atmosphere. Therefore, their accurate estimation is of crucial importance for a better understanding of land surface-atmosphere exchange processes and obtaining the heat and moisture budgets. Different approaches have been developed to estimate turbulent heat fluxes (i.e., H and LE). A number of studies used time-series of air temperature and specific humidity observations to estimate turbulent heat fluxes. These works require the specification of surface roughness lengths for heat and momentum and/or ground heat flux, which are often unavailable. This study estimates turbulent heat fluxes and the atmospheric boundary layer (ABL) height, potential temperature, and humidity by assimilating sequences of air temperature and specific humidity into an atmospheric boundary layer model within a new variational data assimilation (VDA) framework. The unknown parameters of the VDA system are neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). It needs neither the surface roughness parameterization nor ground heat flux measurements. The performance of the developed VDA approach is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site for the summer of 1987 and 1988. The results show that the developed VDA framework is capable of estimating the unknown parameters (i.e., EF and CHN) reasonably well. The developed VDA model can predict the turbulent heat fluxes fairly accurately at the FIFE site. In addition, the ABL height, specific humidity, and potential temperature estimates from the VDA system are reasonably close to those inferred from the radiosondes both in terms of magnitude and diurnal trend. The introduced VDA framework is advanced by the synergistic assimilation of LST, air temperature and specific humidity into a coupled land surface-ABL model. The augmented VDA system is also validated at the FIFE sites. It outperforms the previous study in which air temperature and specific humidity were assimilated. Finally, both developed VDA approaches are tested at five sites (namely, Desert, Audubon, Bondville, Brookings, and Willow Creek) with contrasting climatic and vegetative conditions. The results show that the first VDA system (that assimilates reference-level air temperature and specific humidity) performs well at wet/densely vegetated sites (e.g., Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet and/or densely vegetated sites than the dry and/or slightly vegetated sites. The second VDA approach (that assimilates LST, reference-level air temperature and specific humidity) outperforms the first approach that assimilated only the state variables of atmosphere (i.e., reference-level air temperature and humidity), and can accurately estimate turbulent heat fluxes over a wide variety of environmental conditions.Ph.D
Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change
No full textFluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?
No full textThe potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
No full textElectrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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