Accurate ahead evapotranspiration forecasting is crucial for irrigation planning, for wetlands, agricultural and forest habitats preservation, and for water resource management. Deep learning ...algorithms can be used to develop effective forecasting models of ahead evapotranspiration. In this study, three Recurrent Neural Network-based models were built for the prediction of short term ahead actual evapotranspiration. Two variants of each model were developed changing the employed algorithm, selecting between long short-term memory (LSTM) and nonlinear autoregressive network with exogenous inputs (NARX), while the modeling was performed in the context of an ensemble approach. The prediction models were trained and tested using data from two sites with different climates: Cypress Swamp, southern Florida, and Kobeh Valley, central Nevada. With reference to the subtropical climatic conditions of South Florida, LSTM models proved to be more accurate than NARX models, while some exogenous variables such as sensible heat flux and relative humidity did not affect the results significantly. An increase of the forecast horizon from 1 to 7 days resulted in a slight reduction in the accuracy of both the LSTM- and NARX based models. Considering instead the semi-arid climate of Central Nevada, NARX models generally provided more accurate results, which were only slightly affected by relative humidity, sensible heat flux, and forecast horizon. On the other hand, LSTM models performance decayed if sensible heat flux and relative humidity were neglected, and if the forecast horizon was increased from 1 to 7 days. Deep learning-based models can provide very accurate predictions of actual evapotranspiration, but the performance of the models can be significantly affected by local climatic conditions.
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•Deep learning-based models can give accurate forecasting of actual evapotranspiration.•Models were developed using an ensemble approach.•Forecasting models were compared in subtropical and semiarid climatic conditions.•The performance of the models is depending on the algorithm, forecast horizon, and climate.
•Three evapotranspiration models have been compared, differing in input variables.•Four variants of each model were applied, varying the machine learning algorithm.•M5P Regression Tree, Bagging, ...Random Forest and Support Vector Regression were used.•Data refers to an experimental site in Central Florida, with humid subtropical climate.
The constant need to increase agricultural production, together with the more and more frequent drought events in many areas of the world, requires a more careful assessment of irrigation needs and, therefore, a more accurate estimation of actual evapotranspiration. In recent years, several water management issues have been addressed by means of models derived from Artificial Intelligence research. When using machine learning based models, the main challenging aspects are represented by the choice of the best possible algorithm, the choice of adequately representative variables and the availability of appropriate data sets.
Machine learning algorithms may be a powerful tool for the prediction of actual evapotranspiration, when a time series of few years is available. Starting from the measurements of a sufficient number of climatic parameters it is possible to obtain forecasting models characterized by very high accuracy and precision. Three different evapotranspiration models have been compared in this study. The models differ in the input variables. Four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression. The data refers to an experimental site in Central Florida, characterized by humid subtropical climate.
The best outcomes have been provided by Model 1, whose input variables were net solar radiation, sensible-heat flux, moisture content of the soil, wind speed, mean relative humidity and mean temperature. Model 3, built from data only of mean temperature, mean relative humidity and net solar radiation, has provided still satisfactory results. Model 2, which adds the wind speed to the input variables of Model 3, has provided results that are absolutely comparable to those of Model 3 itself.
In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water ...resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R
above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts.
Stormwater runoff is often contaminated by human activities. Stormwater discharge into water bodies significantly contributes to environmental pollution. The choice of suitable treatment technologies ...is dependent on the pollutant concentrations. Wastewater quality indicators such as biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total suspended solids (TSS), and total dissolved solids (TDS) give a measure of the main pollutants. The aim of this study is to provide an indirect methodology for the estimation of the main wastewater quality indicators, based on some characteristics of the drainage basin. The catchment is seen as a black box: the physical processes of accumulation, washing, and transport of pollutants are not mathematically described. Two models deriving from studies on artificial intelligence have been used in this research: Support Vector Regression (SVR) and Regression Trees (RT). Both the models showed robustness, reliability, and high generalization capability. However, with reference to coefficient of determination R2 and root-mean square error, Support Vector Regression showed a better performance than Regression Tree in predicting TSS, TDS, and COD. As regards BOD5, the two models showed a comparable performance. Therefore, the considered machine learning algorithms may be useful for providing an estimation of the values to be considered for the sizing of the treatment units in absence of direct measures.
We compared diagnostic performance of Magnetic Resonance (MR), Computed Tomography (CT) and Ultrasound (US) with (CEUS) and without contrast medium to identify peribiliary metastasis.
We identified ...35 subjects with histological proven peribiliary metastases who underwent CEUS, CT and MR study. Four radiologists evaluated the presence of peribiliary lesions, using a 4-point confidence scale. Echogenicity, density and T1-Weigthed (T1-W), T2-W and Diffusion Weighted Imaging (DWI) signal intensity as well as the enhancement pattern during contrast studies on CEUS, CT and MR so as hepatobiliary-phase on MRI was assessed.
All lesions were detected by MR. CT detected 8 lesions, while US/CEUS detected one lesion. According to the site of the lesion, respect to the bile duct and hepatic parenchyma: 19 (54.3%) were periductal, 15 (42.8%) were intra-periductal and 1 (2.8%) was periductal-intrahepatic. According to the confidence scale MRI had the best diagnostic performance to assess the lesion. CT obtained lower diagnostic performance. There was no significant difference in MR signal intensity and contrast enhancement among all metastases (p>0.05). There was no significant difference in CT density and contrast enhancement among all metastases (p>0.05).
MRI is the method of choice for biliary tract tumors but it does not allow a correct differential diagnosis among different histological types of metastasis. The presence of biliary tree dilatation without hepatic lesions on CT and US/CEUS study may be an indirect sign of peribiliary metastases and for this reason the patient should be evaluated by MRI.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
United Kingdom (UK).
A regional investigation of the Standard Precipitation Index (SPI) trends and abrupt changes in the UK has been carried out. The K-means algorithm was employed to partition the ...study area into six homogeneous regions, each distinguished by specific SPI characteristics. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were used to evaluate the overall trends for each cluster and SPI time scale, as well as to identify abrupt changes in trend and seasonality along the SPI time series, respectively.
The seasonal MK test revealed statistically significant increasing SPI trends for all clusters, except for the southeastern area of the UK, where decreasing, but not statistically significant, SPI trends were observed. Moreover, despite a scenario suggesting an increasingly humid UK, the BEAST analysis allowed the detection of decreasing abrupt changes in trends, resulting in sudden changes from wet to dry conditions, that cannot be identified using the MK test. Alongside these, the BEAST analysis has also revealed positive abrupt changes in trends across all UK, as well as positive or negative variations in seasonality, which are followed by longer or shorter wet or dry periods, respectively. Overall, the study approach provides a detailed picture of the SPI trends and abrupt changes, in light of the impact of climate change on the different areas of the UK.
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•The study proposed a regional investigation of the SPI trends and abrupt changes in the UK.•A cluster-based and Bayesian ensemble algorithms approach assessed trends and abrupt changes along the SPI time series.•Mann-Kendall revealed increasing SPI trends across UK, while BEAST detects decreasing abrupt changes in trends.
The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values ...of the tide have a considerable practical interest is the city of Venice. The MOSE (MOdulo Sperimentale Elettromeccanico) system was created to protect Venice from flooding caused by the highest tides. Proper operation of the protection system requires an adequate forecast model of the highest tides, which is able to provide reliable forecasts even some days in advance. Nonlinear Autoregressive Exogenous (NARX) neural networks are particularly effective in predicting time series of hydrological quantities. In this work, the effectiveness of two distinct NARX-based models was demonstrated in predicting the extreme values of high tides in Venice. The first model requires as input values the astronomical tide, barometric pressure, wind speed, and direction, as well as previously observed sea level values. The second model instead takes, as input values, the astronomical tide and the previously observed sea level values, which implicitly take into account the weather conditions. Both models proved capable of predicting the extreme values of high tides with great accuracy, even greater than that of the models currently used.
Air entrainment phenomena have a strong influence on the hydraulic operation of a plunging drop shaft. An insufficient air intake from the outside can lead to poor operating conditions, with the ...onset of negative pressures inside the drop shaft, and the choking or backwater effects of the downstream and upstream flows, respectively. Air entrainment phenomena are very complex; moreover, it is impossible to define simple functional relationships between the airflow and the hydrodynamic and geometric variables on which it depends. However, this problem can be correctly addressed using prediction models based on machine learning (ML) algorithms, which can provide reliable tools to tackle highly nonlinear problems concerning experimental hydrodynamics. Furthermore, hybrid models can be developed by combining different machine learning algorithms. Hybridization may lead to an improvement in prediction accuracy. Two different models were built to predict the overall entrained airflow using data obtained during an extensive experimental campaign. The models were based on different combinations of predictors. For each model, four different hybrid variants were developed, starting from the three individual algorithms: KStar, random forest, and support vector regression. The best predictions were obtained with the model based on the largest number of predictors. Moreover, across all variants, the one based on all three algorithms proved to be the most accurate.
In the Venice Lagoon some of the highest tides in the Mediterranean occur, which have influenced the evolution of the city of Venice and the surrounding lagoon for centuries. The forecast of “high ...waters” in the lagoon has always been a matter of considerable practical interest. In this study, tide prediction models were developed for the entire lagoon based on Nonlinear Autoregressive Exogenous (NARX) neural networks. The NARX-based model development was performed in two different stages. The first stage was the training and testing of the NARX network, performed on data collected in a given time interval at the tide gauge of Punta della Salute, at the end of Canal Grande. The second stage consisted of a comprehensive validation of the model in the entire Venice Lagoon, with a detailed analysis of data from three measuring stations located in points of the lagoon with different characteristics. Good predictions were achieved regardless of whether the meteorological parameters were considered among input parameters, even with considerable time advance. Furthermore, the forecasting model based on NARX has proved capable of predicting even exceptional high tides. The proposed model could be a useful support tool for the management of the MOSE system, which will protect Venice from high waters.
Nowadays, drought phenomena increasingly affect large areas of the globe; therefore, the need for a careful and rational management of water resources is becoming more pressing. Considering that most ...of the world’s unfrozen freshwater reserves are stored in aquifers, the capability of prediction of spring discharges is a crucial issue. An approach based on water balance is often extremely complicated or ineffective. A promising alternative is represented by data-driven approaches. Recently, many hydraulic engineering problems have been addressed by means of advanced models derived from artificial intelligence studies. Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. The machine learning models have proven to be able to provide very encouraging results. M5P provides good short-term predictions of monthly average flow rates (e.g., in predicting average discharge of the spring after 1 month, R2=0.991, RAE=14.97%, if a 4-month input is considered), while RF is able to provide accurate medium-term forecasts (e.g., in forecasting average discharge of the spring after 3 months, R2=0.964, RAE=43.12%, if a 4-month input is considered). As the time of forecasting advances, the models generally provide less accurate predictions. Moreover, the effectiveness of the models significantly depends on the duration of the period considered for input data. This duration should be close to the aquifer response time, approximately estimated by cross-correlation analysis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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