Set Of Parameters Research Articles

A theoretical framework for BL Her stars. II. New period-luminosity relations in Gaia passbands

In the era of the Hubble tension, it is crucial to obtain a precise calibration of the period-luminosity ($PL$) relations of classical pulsators. Type II Cepheids (T2Cs; often exhibiting negligible or weak metallicity dependence on $PL$ relations) used in combination with RR Lyraes and the tip of the red giant branch may prove useful as an alternative to classical Cepheids for the determination of extragalactic distances. We present new theoretical $PL$ and period-Wesenheit ($PW$) relations for a fine grid of convective BL Her (the shortest period T2Cs) models computed using mesa-rsp in the $Gaia$ passbands and we compare our results with the empirical relations from $Gaia$ DR3. Our goal is to study the effect of metallicity and convection parameters on the theoretical $PL$ and $PW$ relations. We used the state-of-the-art 1D non-linear radial stellar pulsation tool mesa-rsp to compute models of BL Her stars over a wide range of input parameters:\ metallicity ($-2.0\; dex Fe/H dex $), stellar mass ($0.5M_ odot -0.8M_ odot $), stellar luminosity ($50L_ odot -300L_ odot $), and effective temperature (across the full extent of the instability strip; in steps of 50K). We used the Fourier decomposition technique to analyse the light curves obtained from mesa-rsp and $Gaia$ DR3 and then compared the theoretical and empirical $PL$ and $PW$ relations in the $Gaia$ passbands. The BL Her stars in the All Sky region exhibit statistically different $PL$ slopes compared to the theoretical $PL$ slopes computed using the four sets of convection parameters. We find the empirical $PL$ and $PW$ slopes from BL Her stars in the Magellanic Clouds to be statistically consistent with theoretical relations computed using the different convection parameter sets in the $Gaia$ passbands. There is a negligible effect coming from the metallicity on the $PL$ relations in the individual $Gaia$ passbands. However, there is a small but significant negative coefficient of metallicity in the $PWZ$ relations for the BL Her models using the four sets of convection parameters. This could be attributed to the increased sensitivity of bolometric corrections to metallicities at wavelengths shorter than the $V$ band. Our BL Her models also suggest a dependence of the mass-luminosity relation on metallicity. We found the observed Fourier parameter space to be covered well by our models. Higher mass models ($> 0.6\ odot $) may be needed to reliably model the observed light curves of BL Her stars in the All-Sky region. We also found the theoretical light curve structures (especially the Fourier amplitude parameters) to be affected by the choice of convection parameters.

An equivalent point-source stochastic model of the NGA-East ground-motion models

The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.

Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin

Abstract This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. In particular, the BILAN model, which is based on a more complex set of parameters, showed better overall results in simulating dry conditions, while the GR2M model had superior performance in wet conditions. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. Extended calibration periods for both models narrowed the range of the Kling-Gupta Efficiency (KGE) values and reduced the loss of performance during the parameter transfer from calibration to validation. For the BILAN model, a longer calibration period also significantly reduced the variability of performance metric values. Conversely, for the GR2M model, the variability rate did not decrease with the length of the calibration periods. Testing both models under variable conditions underscored the crucial role of comprehending model structure, hydrological sensitivity, and calibration strategy effects on simulation accuracy and uncertainty for reliable results.

Modeling of Average Nusselt Number by Machine Learning and Interpolation Techniques

Abstract In this study, an important heat transfer, fluid flow parameter, and average Nusselt number Nu¯ are statistically modeled by using the data obtained from a numerical process. The two-dimensional, time-dependent dimensionless equations of natural convection (NC) flow either in the absence or in the presence of a uniform inclined magnetic field (MF) are numerically solved by using global radial basis function (RBF) method in spatial derivatives and the second-order backward differentiation formula (BDF2) in time derivatives. Numerical simulations are performed in a set of combined dimensionless problem parameters. A dataset with inputs Rayleigh number Ra, Prandtl number Pr, and output Nu¯ in the absence of MF and a dataset with inputs Ra, Pr, Hartmann number Ha, inclination angle γ, and output Nu¯ in the presence of inclined uniform MF are saved. The obtained data are separated into train and test sets. Then, Nu¯ is first modeled by Neural Networks (NN). Second, interpolation is also examined. In terms of mean squared error (MSE) metric, NN outputs give the best goodness of fit results compared to curve fitting on test data. On the other side, it is shown that interpolation is also an alternative for modeling. This modeling issue enables one to get the desired result without making heavy numerical calculations many times.

Research on the Influence of Deep-Water Drilling Risers on Drillstring Motion Trajectory and Vibration Characteristics

The swing of the riser in deep-water drilling can significantly impact the drill string. In this study, we establish a riser model that considers the combined disturbance of periodic dynamic wind and wave loads. By coupling it with the drill string model, we develop a dynamic model for deep-water drilling systems. Through analyzing multiple sets of different drilling parameters, we examine displacements and impact forces at various positions along the drill string system. Specifically, our focus lies on velocity, acceleration, and rotational speed information of BHA. We investigate how WOB and rotational speed influence motion trajectory and vibration characteristics of the drill string within the dynamic model of deep-water drilling systems. Simulation results reveal slight differences in whirling trajectories between inside the riser and below mud line for the drill string. Rotational speed has a greater impact on the drill string compared to WOB; higher rotational speeds lead to increased collision forces between the drill string system and both riser and wellbore. Our findings identify specific combinations of WOB and rotational speed parameters that can stabilize drilling operations within dynamic models for deep-water drilling systems. These research results provide valuable insights for adjusting WOB and rotational speed parameters in deep-water drilling.

Optimization of Charpy Impact Strength of Tough PLA Samples Produced by 3D Printing Using the Taguchi Method.

This research employs the Taguchi method and analysis of variance (ANOVA) to investigate, analyze, and optimize the impact strength of tough polylactic acid (PLA) material produced through fused deposition modeling (FDM). This study explores the effect of key printing parameters-specifically, infill density, raster angle, layer height, and print speed-on Charpy impact strength. Utilizing a Taguchi L16 orthogonal array experimental design, the parameters are varied within defined ranges. The results, analyzed through signal-to-noise (S/N) ratios and ANOVA, reveal that infill density has the most substantial impact on Charpy impact strength, followed by print speed, layer height, and raster angle. ANOVA identifies infill density and print speed as the most influential factors, contributing 38.93% and 36.51%, respectively. A regression model was formulated and this model predicted the impact strength with high accuracy (R2 = 98.16%). The optimized parameter set obtained through the Taguchi method, namely, a 100% infill density, 45/-45° raster angle, 0.25 mm layer height, and 75 mm/s print speed, enhances the impact strength by 1.39% compared to the experimental design, resulting in an impact strength of 38.54 kJ/m2. Validation experiments confirmed the effectiveness of the optimized parameters.

Meticulous Molecular Engineering of Crystal Orientation and Morphology in Conjugated Polymer Thin Films for Field-Effect Transistors.

The ability to precisely tailor molecular packing and film morphology in conjugated polymers offers a robust means to control their optoelectronic properties. This, however, remains a grand challenge. Herein, we report the dependency of molecular packing of an important conjugated polymer, poly(2,5-bis(3-alkylthiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT), on a set of intrinsic parameters and unveil the correlation between their crystalline structures and charge transport characteristics. Specifically, a family of PBTTT with varying side chains (i.e., hexyl, octyl, decyl, dodecyl, tetradecyl, and hexadecyl referred to as C6, C8, C10, C12, C14, and C16, respectively) and molecular weights (MWs) with a focus on C14 are judiciously designed and synthesized. Various crystalline structures are yielded by tuning the alkyl chain and MW of PBTTT together with thermal annealing. It reveals that extending the alkyl chain length of PBTTT to C14, along with a larger MW and heating at 180 °C, promotes the formation of edge-on crystallites with significantly improved orientation and ordering. Furthermore, these distinct crystalline structures greatly impact their charge mobilities. This study sheds light on the tailored design of crystalline structures in PBTTT through a synergetic approach, which paves the way for potential applications of PBTTT and other conjugated polymers in optoelectronic devices with enhanced performance.

Process parametric optimization of fused deposition modeling for manufacturing of acrylonitrile butadiene styrene parts

Additive manufacturing has been a revolution in the last decade and has led to a number of product innovations. Rapid prototyping (RP) has significantly reduced the product development cycle time and RP techniques are now replacing many conventional plastic processing techniques where production volume is not an issue. In order to ascertain the utility of additive manufacturing techniques for the development of fully functional parts, it’s important to establish the optimal set of process parameters for achieving maximum mechanical performance. In the current experimental investigation, the dogbone-shaped parts of acrylonitrile butadiene styrene (ABS) were fabricated by the fused deposition modeling (FDM) process. The process parameters were optimized for dogbone-shaped specimens fabricated by the FDM process. Among the parameters, it has been found that orientation and infill density are the most dominant factors affecting the tensile strength of FDM parts printed with ABS material.

Application of CALPHAD Method for Predicting of Concentration Range of Amorphization of Transition Metals Melts

Early, the efficiency of the CALPHAD (Calculation of Phase Diagrams) method to a targeted search for compositions of amorphous alloys has been shown. The method for predicting the ranges of amorphization is based on the calculation of diagrams of metastable phase transformations between supercooled melts and boundary solid solutions on the base of pure elements. In this work, the model parameters for thermodynamic properties of liquid alloys and boundary solid solutions were summarized in a self-consistent database for the multicomponent Cu–Fe–Ni–Ti–Zr–Hf system. Such database for the multicomponent system is based on a common set of model parameters for boundary binary and ternary systems. This database was used to predict the concentration ranges of amorphization for the quinary Cu–Fe–Ni–Ti–Zr, Cu–Fe–Ni–Ti–Hf and boundary ternary and quaternary systems. The results of calculations are presented along sections in quaternary and quinary systems. The ternary and quaternary equiatomic alloys along with high entropy CuFeNiTiZr and CuFeNiTiHf alloys are trapped into prognosed composition ranges of amorphization. Predicted composition space of amorphization for melts of the Fe–Ni–Ti–Zr system is shown on the concentration tetrahedron. Based on the obtained results, a new criterion for predicting the concentration regions of amorphization of multicomponent melts is proposed, according to which the presence of a sufficient content of metals that are electron acceptors and donors is a chemical factor that affects the thermodynamic stability of melts and determines their glass-forming ability. For multicomponent melts of the Cu–Fe–Ni–Ti–Zr–Hf system the concentration ranges of amorphization correspond to the simultaneous fulfillment of the conditions xFe + xNi + xCu > 0.25 and xTi + xZr + xHf > 0.15, where Fe, Ni, and Cu are electron acceptors and Ti, Zr, and Hf are electron donors.

Technical note: Bimodal parameterizations of in situ ice cloud particle size distributions

Abstract. The cloud particle size distribution (PSD) is a key parameter for the retrieval of microphysical and optical properties from remote-sensing instruments, which in turn are necessary for determining the radiative effect of clouds. Current representations of PSDs for ice clouds rely on parameterizations that were largely based on aircraft in situ measurements where the distribution of small ice crystals were uncertain. This makes current parameterizations deficient to simulate remote-sensing observations sensitive to small ice, such as from lidar and thermal infrared instruments. In this study we fit the in situ PSDs of ice crystals from the JULIA (JÜLich In situ Aircraft data set) database, which consists of 11 campaigns covering the tropics, midlatitudes and the Arctic, consistently processed and considered more robust in their measurements of small ice. For the fitting, we implement an established approach to PSD parameterizations, which consists of finding an adequate set of parameters for a modified gamma function after normalization of both PSD axes. These parameters are constrained to match in situ measurements when predicting microphysical properties from the PSDs, via a cost function minimization method. We selected the ice water content and the ice crystal number concentration, which are currently key parameters for modern satellite retrievals and model microphysics schemes. We found that a bimodal parameterization yields better results than a monomodal one. The bimodal parameterization has a lower spread for almost all ice crystal sizes over the entire range of analyzed temperatures and fits better the observations, especially for particles between 20 and about 110 µm at temperatures between −60 and −20 ∘C. For this temperature range, the root mean square error for the retrieved Nice is reduced from 0.36 to 0.20. This demonstrates a clear advantage to considering the bimodality of PSDs, e.g., for satellite retrievals.

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping.

A reactive force field approach to modeling corrosion of NiCr alloys in molten FLiNaK salts

The interface between NiCr alloys and FLiNaK molten salt exhibits complex corrosion behavior, mainly driven by intricate chemical interactions involving Cr and F−ions. Understanding these dynamic reactions is crucial for developing effective corrosion mitigation strategies to ensure the long-term durability of Ni-based alloy components in molten salt technologies. However, obtaining molecular-level understanding through experiments is challenging. To address this, we utilize reactive molecular dynamics simulations enabled by a reactive force field, ReaxFF, to investigate detailed reaction dynamics at the atomic level. We first present the development of the ReaxFF parameter set for Ni/Cr/F/Li/Na/K based on extensive first-principles calculations. With this force field, we achieve a strong agreement for the structure of FLiNaK molten salt by comparing the pair distribution functions with experimental and simulation results. Furthermore, our simulations successfully reproduce the experimental phenomenon of Cr dissolution in fluoride salt, with the corrosion rate depending on the alloy and salt compositions. Particularly, our simulations reveal that increasing the concentration of Li can enhance the formation of a compact double layer, mitigating Cr dissolution. This work enables a fundamental understanding of the interfacial behavior between fluoride salt and NiCr alloys.

Single-View 3D Reconstruction via Differentiable Rendering and Inverse Procedural Modeling

Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image.

Multidimensional Multi-Connected Fuzzy Interval-Logic Controller

The key features and the principle of operation of a multidimensional fuzzy interval-logic controller with interconnected adjustable parameters developed by the authors are considered. The scheme of interpretation of continuous physical quantities by an equivalent set of terms and the basic version of the block diagram of the regulator are presented. The main elements of the logical output block are described, including the block of production rules, which includes a predefined database of values of control actions on control objects in the form of ordinal numbers of terms of output continuous quantities. The advantages and features of setting up the mutual connections between the input and output continuous values of the regulator are considered, which is expressed in determining the functional dependencies between them. The set of parameters possessed by the terms of input continuous quantities in relation to specific output continuous quantities of the regulator is described. The methods of defuzzification of the values of output continuous quantities within the boundaries of their terms are shown. The possibility of combining input and output continuous quantities into groups when setting up mutual connections between them is described. The mechanism of determining groups of interrelated continuous quantities is illustrated, the total number of which is determined by forming a matrix of relationships, according to the author’s scheme. The algorithm of sequential processing of the contents of the cells of the matrix of relationships, consisting of three steps, is considered. Expressions are given for calculating the basic parameters of a multidimensional fuzzy interval-logic controller with interrelated adjustable parameters. The results of a computational experiment to reduce the system of production rules of the regulator in determining the relationships between input and output continuous quantities are presented.

Addressing the Kaizen business operations: the role of triple helix actors during COVID-19 outbreak

PurposeThe aim of this study is to investigate and clarify how the triple helix actors can effectively implement the concepts of Kaizen to navigate and overcome the complex obstacles brought on by the global COVID-19 pandemic.Design/methodology/approachThrough broad literature reviews, nine common parameters under triple helix actor have been recognized. A regression analysis has been done to study how the triple helix actors’ common parameters impact Kaizen implementation in business operations.FindingsThe results of this study revealed insightful patterns in the relationships between the common parameters of triple helix actor and the dependent variables. Notably, the results also showed that leadership commitment (LC) emerges as a very significant component, having a big impact on employee engagement as well as organizational performance.Research limitations/implicationsIn addition to offering valuable insights, this study has limitations including the potential for response bias in survey data and the focus on a specific set of common parameters, which may not encompass the entirety of factors influencing Kaizen implementation within the triple helix framework during the pandemic.Originality/valueThe originality of this study lies in its comprehensive exploration of the interplay between triple helix actors and Kaizen principles in addressing COVID-19 challenges. By identifying and analyzing nine specific common parameters, the study provides a novel framework for understanding how triple helix actors collaboratively enhance organizational performance and employee engagement during challenging times.

Search for duplicates of showers in the IAU MDC database

Context. The meteor shower database of the IAU Meteor Data Center (MDC) is used by the whole community of meteor astronomers. Observers submit both new and known meteor shower parameters to the MDC. Two types of problems may arise during the submission process: If a new observation of an already-known meteor shower is submitted as the discovery of a new shower, a duplicate shower will appear in the MDC. If the submission of a new set of parameters for an existing shower is incorrect, a false duplicate of a known meteor shower will appear in the MDC. The MDC database contains such duplicates and false duplicates, so it is desirable to detect them among the streams already in the database and those delivered to the database as new streams. Aims. We aim to develop a method for objective detection of duplicates among meteor showers and to apply it to the MDC. The method will also enable us to verify whether various sets of parameters of the same shower are compatible and thus reveal the false duplicates. Methods. We suggest two methods based on cluster analyses and two similarity functions among geocentric and heliocentric shower parameters collected in the MDC. Results. We obtained a number of results of varying significance. Seven new showers represented by two or more parameter sets were discovered, revealing the duplicates we searched for. We found full agreement between our results and those reported in the MDC database for 30 showers. The multiple sets of parameters defining these showers are correct since they were identified as duplicates. For 20 showers, the same duplicates as given in the MDC were found only by one method. We found 27 showers for which the number of parameter sets found by both methods is close to the corresponding number in the MDC database. However, we found 56 showers listed in the MDC by more than one set of parameters for which no duplicates were found by either of the applied methods. These showers have false duplicates among their sets of parameters. Conclusions. The obtained results confirm the effectiveness of the proposed approach of identifying duplicates. We have shown that in order to detect and verify duplicate meteor showers, it is possible to apply the objective proposal instead of the subjective approach used so far. We consider the identification of 83 problematic cases in the MDC database, among which at least some duplicates were misclassified, to be a particularly important result. The correction of these cases will significantly improve the content of the MDC database.

AI-Based System Automates Textual Classification of Daily Drilling Reports

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32978, “Development and Implementation of an AI-Based System To Automate Textual Classification on Daily Drilling Reports,” by Stephan Perrout, Aliel F. Riente, and Guilherme S.F. Vanni, SPE, Petrobras, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ Structured daily drilling reports (DDRs) are a rich source of information that allows better planning, more-accurate risk analysis, and improved key performance indicators and contracts. However, such information is originally stored in a free-text and unstructured format, which becomes difficult for efficient data mining. With the advance of artificial intelligence (AI) technologies, particularly AI language models, applying such techniques over unstructured data has become critical to digital transformation. The complete paper presents an approach for automatic DDR classification that incorporates new techniques of AI. Introduction This work addresses the complex task of automatic classification of DDRs according to a newly proposed ontology. The ontology follows a hierarchical model that classifies actions into three or four levels depending on the intervention, considering drilling, completion, and abandonment. Each event has an ontology built and reviewed by experts in oil and gas. Classifying DDR constitutes a demanding task, and effectively exploiting AI-based models represents a promising solution. This work bridges the gap by proposing a classifier based on transformers along with recurrent neural networks (RNNs) to classify reported events described in unstructured text related to drilling, completion, and abandonment interventions. A large number of DDRs was used for training and validation of the proposed classifier, yielding promising results for key processes in the company. Neural-Network Techniques Bidirectional Long Short-Term Memory. Early neural-network models are characterized by inputs of fixed length. This is a drawback when working with texts, however, because sentences vary in their number of words. To overcome such an issue and to process data sequentially, RNNs were proposed. The RNNs are characterized by a set of parameters inherent from the early models plus an internal memory (a hidden or internal state) responsible for storing the context of the sequence being processed. Long short-term memory (LSTM) is a variation of RNN proposed to mitigate two problems: Information can be easily lost when processing very long sequences, and the gradient can become quite low because of the high number of mathematical operations performed during the processing while remaining far from reaching the threshold. LSTM consists of a set of parameters called the input gate, forget gate, and output gate that control information flow through the network. This set of additional parameters helps to maintain only what is important for the internal state of the network besides controlling the output. BiLSTM is a variant of LSTM that comprises two LSTMs. One processes texts from left to right, and the second one processes texts from right to left. This feature allows “future” elements to be part of the model’s decision process for “past” elements. The final classification is the combination of the output of both LSTMs.

Extended Morlet Wavelet-Based FIR Phasor Estimation Using Fake Samples

The paper proposes an extended Morlet wavelet-based FIR (et-MW-FIR) which can be piled on top of enhanced MW-FIR (e-MW-FIR) to further improve the overall estimation accuracy. Moreover, by integrating the extended algorithm block, the wavelet parameter selection range is enlarged. In this case, as one application of the proposed method, the paper also introduces a fully compliant P-class estimator by varying the parameters rather than using a threshold parameter which may lead to non-convergence in practice. The new estimators with two sets of parameters are compared with the conventional and enhanced estimators under IEC/IEEE 60255-118-1. Additionally, some extensive simulations, field data, and hardware implementation are also conducted to evaluate the performance of the proposed block and estimator.

Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms

Groundwater quality is one of the major concerns. Quality of the groundwater directly impacts human health, growth of plants and vegetables. Due to the severe impacts of inadequate water quality, it is imperative to find a swift and economical solution. Water quality prediction may help us to manage water resources properly. The present study has been carried out considering thirty-seven water sample data points form the Pindrawan tank command area of Raipur district, Chhattisgarh, India. A total of nineteen physicochemical parameters were measured, out of which seventeen parameters were used to compute the weight-based groundwater quality index (WQI). In this present work, the primary goal is to identify the most effective parameters for WQI prediction. Out of the seventeen parameters tested, the Mann—Whitney—Wilcoxon (MWW) statistical test has revealed that five parameters Fe, Cr, Na, Ca, and Mg hold a strong statistical significance in distinguishing between drinkable and non-drinkable water. Out of these five parameters, Cr is the only parameter that maintains a different range of values for drinkable water and non-drinkable water. To validate the efficiency of these statistically significant parameters, machine learning techniques like Artificial Neural Networks (ANN) and Logistic Regression (LR) were used. The experimental results clearly demonstrate that out of all the seventeen parameters tested, utilizing only Cr yields remarkably high classification accuracy. ‘Cr’ achieved an accuracy of 91.67% using artificial neural networks. This is much higher than the accuracy of 66.67% obtained using a parameter set with all seventeen parameters. The proposed methodology achieved good accuracy when classifying water samples into drinkable and non-drinkable water using only one parameter, ‘Cr’.

Nonlinear multi independent variables in quantifying river bank erosion using Neural Network AutoRegressive eXogenous (NNARX) model

This study proposed a novel application of Neural Network AutoRegressive eXogenous (NNARX) model in predicting nonlinear behaviour of riverbank erosion rates which is difficult to be achieved with good accuracy using conventional approaches. This model can estimate complex river bank erosion rates with flow variations. The NNARX model analysed to a set of primary data, 60% (203 data for training) and 40% (135 data for testing), which were collected from Sg. Bernam, Malaysia. A set of nondimensional parameters, known as functional relationship, used as an input to the NNARX model has been established using the method of repeating variables. The One-Step-Ahead time series prediction plots are used to assess the accuracy of all developed models. Model no. 6 (5 independent variables with 10 hidden layers) gives good predictive performance, supported by the graphical analysis with discrepancy ratio of 94% and 90% for training and testing datasets. This finding is consistent with model accuracy result, where Model no. 6 achieved R2 of 0.932 and 0.788 for training and testing datasets, respectively. Result shows that bank erosion is maximized when the near-bank velocity between 0.2 and 0.5 m/s, and the riverbank erosion is between 1.5 and 1.8 m/year. On the other hand, higher velocities ranging from 0.8 to 1.3 m/s induces erosion at a rate between 0.1 and 0.4 m/year. Sensitivity analysis shows that the highest accuracy of 91% is given by the ratio of shear velocity to near-bank velocity followed by boundary shear stress to near-bank velocity ratio (88.5%) and critical shear stress to near-bank velocity ratio (88.2%). It is concluded that the developed model has accurately predicted non-linear behaviour of riverbank erosion rates with flow variations. The study's findings provide valuable insights in advanced simulations and predictions of channel migration, encompassing both lateral and vertical movements, the repercussions on the adjacent river corridor, assessing the extent of land degradation and in formulating plans for effective riverbank protection and management measures.