Calibration and evaluation of six popular evapotranspiration formula based on the Penman-Monteith model for continental climate in Turkey

Due to the difficulties in measuring and collecting meteorological data, it may be necessary to use methods that allow for the estimation of reference evapotranspiration values by using the few available data. For this reason, it is useful to calibrate empirical formulas locally, which enables obtaining accurate and reliable estimates with a small number of data. The effects of other parameters excluded from the employed data are included in the formula as regional coefficients. In this study, six empirical evapotranspiration formulas among temperature-based and solar radiation-based methods, including Thornthwaite (1948), Makkink (1957), Turc (1961), Jensen and haise (1963), Priestley and Taylor (1972), and Hargreaves and samani (1985) are calibrated, for continental climates of Central Anatolia's. To this end, meteorological data of three synoptic stations located in the west of Turkey in Kutahya province are employed from 2007 through 2019. Moreover, the data of the fourth station are applied for evaluating the accuracy of the estimated results. The main reason for employing these six formulas are behind in the fact that they are globally used, easy-to-use, and need few types of data. Meanwhile, Penman-Monteith 56 equation is applied to calibrate the empirical equations. In total, three methods are followed to estimate the local calibration coefficients. Finally, the evaluation of the results is performed by using the coefficient of determination, percentage error of estimate, mean absolute error, and root-mean-square error. According to the evaluation criteria, among calibrated formulas, Jensen and haise's (1963) formula was obtained to be the best for estimating ETo. © 2022 Elsevier Lt

Deep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiography

This paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is performed by using time series of the heart sound signal, so-called Phonocardiography (PCG). The classification objective is to discriminate between healthy and patients with cardiac diseases by applying a deep machine learning method to PCGs. This approach which is called intelligent phonocardiography has received interest from the researchers toward the development of a smart stethoscope for decentralized diagnosis of heart disease. It is found that DTGNN associates further flexibility to the approach which enables the classifier to learn subtle contents of PCG, and meanwhile better copes with the complexities intrinsically that exist in the medical applications such as the imbalance training. The structural risk of the two methods is compared using the A-Test method.publishedVersio

Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification

Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Physionet dataset of heart sound recordings. The accuracy of the parallel architecture of LSTM-CNN reached 98.0% outperforming all the combined architectures, with a sensitivity of 87.2%. The conventional CNN offered sensitivity/accuracy of 95.9%/97.3% with far less complexity. Results show that a conventional CNN can appropriately perform and solely employed for the classification of heart sound signals.publishedVersio

Numerical modeling of groundwater flow based on explicit and fully implicit schemes of finite volume method

This paper documents a novel numerical model for calculating the behavior of unsteady, one-dimensional groundwater flow by using the finite volume method. The developed model determined water table fluctuations for different scenarios as follows: Drainage and recession from an unconfined aquifer, and water table fluctuations above an inclined leaky layer due to ditch recharge with a constant and variable upper boundary condition. The Boussinesq equation, which is the governing equation in this domain, is linearized and solved numerically in both of the explicit and fully implicit conditions. Meanwhile, the upwind scheme is employed to discretize the governing equation. The computed outcomes of both the explicit and implicit approaches agreed well with the results of analytical solution and laboratory experiments. The main reason is that in the first half of simulation process explicit scheme obtains slightly better results and in the second half of the simulation process fully implicit scheme predicts more reliable outcomes that are hidden in the neighbor node points. As a final point, the numerical outcomes confirm that the developed model is capable of calculating satisfactory outcomes in engineering and science applications

Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for screening heart abnormalities.publishedVersio

Seismic evaluation of existing arch dams and massed foundation effects

AbstractIn the present paper, the effects of a massed foundation on the nonlinear seismic response of an existing arch dam are investigated. A co-axial rotating smeared crack approach was used to model the nonlinear behavior of the mass concrete in a 3D space which is able to model cracking/crushing under static and dynamic conditions. The analysis also considered the opening/slipping of joints. The reservoir was assumed to be compressible and was modeled using the finite element method with the appropriate boundary conditions. The Dez arch dam was selected for the case study and excited by a maximum credible earthquake. It was found that assuming a massless foundation leads to the overestimation of the stresses within the dam body and causes many more crack profiles than the massed foundation model. As a result, in the case of a massed foundation, no numerical instability was found to exist during the analysis

Planning effective and efficient public transport systems

As population increases and cities continue to grow, there is an urgency to provide efficient and cost effective Public Transport (PT).  Globally there are enormous differences between transport systems; some countries have efficient systems while others appear to have no system at all.  This research is undertaken with the express purpose of investigating efficient, well incorporated PT systems from around the world, for their specific application to Australian capital city transport hubs, but also for their adaptability to other global areas. The aim of this paper is to develop strategies for planning public transport.The design of this paper relies heavily on extensive global research, seeking to discover appropriate PT systems and then investigating the benefits and feasibility in an Australian context. The paper examines case studies from Europe, Asia and Canada and focuses not only on efficiency and cost effectiveness, but also on sustainability. Case studies from major cities with cost efficient and effective public transport systems were examined and analysed to develop models of PT systems for Australian cities.This research is limited by the large volume of public transport case studies that are available, the limitations on the size of this research paper and the lack of available specific data. The goal is to expand on this introductory research over a sustained period.  This is an original study and although only in its infancy, this research will be of significant value to the Australian public transport industry to support improvements in infrastructure

Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks

Precise estimation of groundwater level (GWL) fluctuations has a substantial effect on water resources management. In the present study, to forecast the regional mean monthly time series groundwater level (GWL) with a range of 4.82 (m) in Urmia plain, three different layer structures of Gated Recurrent Unit (GRU) deep learning-based neural network models via the module of sequence-to-sequence regression are designed. In this sense, 180-time series datasets of regional mean monthly meteorological, hydrological, and observed water table depths of 42 different monitoring piezometers during the period of Oct 2002–Sep 2017 are employed as the input variables. By using Shannon entropy method, the most influential parameters on GWL are determined as regional mean monthly air temperature (Tam), precipitation (Pm), total (sum) water diversion discharge (Wdm) of four main rivers. Nevertheless, Cosine amplitude sensitivity analysis confirmed Tam as a dominant factor. For preventing overfitting problem, an algorithm tuning technique via different kinds of hyperparameters is operated. In this respect, several scenarios are implemented and the optimal hyperparameters are accomplished via the trial-and-error process. As stated by the performance evaluation metrics, Model Grading process, and Total Learnable Parameters (TLP) value, the innovative and unique suggested model (3), entitled GRU2+, (Double-GRU model coupled with Addition layer (+)) with seven layers is carefully chosen as the best model. The unique suggested model (3) in the optimal hyperparameters, resulted in an R2 of 0.91, a total grade (TG) of 7.76, an RMSE of 0.094 (m), and a running time of 47 (s). Thus, the model (3) can be certainly employed as an effective model to forecast GWL in different agricultural areas. © 2022 Elsevier B.V