Singularity Index Research Articles

Coherence-induced polarization effects in vector vortex beams.

We demonstrate theoretically and experimentally coherence-induced polarization changes in higher-order vector vortex beams (VVBs) with polarization singularity. The prominent depolarization on decreasing the transverse correlation width in a focused partially coherent VVB provides a means to shape the intensity profile and degree of polarization (DOP) while preserving the polarization distribution. The intensity variation and DOP dip are found to be dependent on the polarization singularity index of the beam. Our results may provide an additional degree of freedom in myriad applications presently projected with VVBs.

Quantifying the contributions of soil surface microtopography and sediment concentration to rill erosion

Soil surface microtopography plays a significant role in rill erosion. In addition, upslope inflow has a large effect on downslope soil erosion processes. Experiments including four different upslope filling areas (0 m2, 0.15 m2, 0.30 m2, and 0.45 m2) with a upslope inflow rate (6 L min−1) were conducted in two 1 m × 2 m boxes on a 15° slope to examine the effects of microtopography and sediment concentration on rill erosion processes. The upslope filling areas were used to simulate different areas of earthen dike terraces. The results showed that the minimum values of soil surface elevation increased from −120 mm to −110 mm as the upslope filling area increased. The values of the simple fractal dimension (f(α)max), the singular index span (Δα) and the difference of multifractal spectrum (Δf(α)) reached minimum values in the 0.45 m2 upslope filling area. With the development of rill erosion, the soil surface microtopography tended to sharpen, and the relative elevation changed greatly. The runoff and soil loss associated with rill erosion gradually decreased as the upslope filling area increased. We identified the temporal evolution of rill erosion using Morlet wavelet analysis. The main period of temporal fluctuation of rill erosion was 28 min under different upslope filling areas. Multi-scale periods of temporal fluctuation of rill erosion emerged with the increase in upslope filling area. The Δα significantly affected the runoff and soil loss. The proportional contributions of Δα to the runoff and sediment yield were 80.95% and 77.34%, respectively. While the contributions of sediment concentration to runoff and sediment yield were 17.05% and 20.66%, respectively. The findings are important significance for better understanding rill erosion mechanisms of purple soil.

Water quality analysis in a lake using deep learning methodology: prediction and validation

ABSTRACT Discharge of untreated waste water, municipal sewage, industrial effluents, dumping of degradable and non-degradable wastes has polluted natural water sources like river, lake, pond to a great extent, therefore, it is obligatory to test out the quality of water before consumption. With this motivation, the work explores many deep learning algorithms to estimate the Water Quality Index, which is a singular index to describe the general quality of water, and the Water Quality Class, which is a distinctive class defined on the basis of the Water Quality Index. The water samples were collected from Korattur Lake in the Chennai city. The water quality parameters such as pH, Total Dissolved Salts, turbidity, phosphate, nitrate, iron, Chemical Oxygen Demand, chloride and sodium were measured from the collected water samples. The models used for training and testing include Deep Learning models such as Artificial Neural Network, Recurrent Neural Network and Long-Short Term Memory for both binary and multi-class classification. The metrics used for evaluating the models were accuracy, precision and the execution time of the models that are used for comparing and analysing above mentioned models. From the results obtained, it was observed that LSTM yielded the highest accuracy of around 94% and also consumes the least execution time when compared with other deep learning models.

Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data

This work integrated selective principal component analysis (SPCA) with a singularity fractal model to map hydrothermal alterations of argillic, phyllic, and propylitic in the north-west of Kerman city in Iran. SPCA results were provided for short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to map phyllic and propylitic alterations. Also, the bands 5–7 were applied to map the argillic alteration zone. SPCA results could present useful information for alteration mapping but it does not show the purest pixels of alteration. Therefore, a fractal model of singularity was applied to highlight the different alteration pixels in the Kerman Cenozoic magmatic arc (KCMA). Thus, the singularity index of different hydrothermal alteration zones was produced. Comparing the obtained results with the field data showed that Kader, Serenu, Meiduk, and Abdar deposits were acceptably identified by alteration mapping. Also, it seems that the singularity index could not discriminate the hydrothermal alterations of argillic and phyllic. The same spectral signature of kaolinite and muscovite minerals is the main reason for misclassifications.

Polarization rotation and singularity evolution of fundamental Poincaré beams through anisotropic Kerr nonlinearities

We report a theoretical investigation on polarization rotation and singularity evolution of focused fundamental Poincaré beams in free-space propagation and through isotropic and anisotropic Kerr nonlinear media. It is found that the optical nonlinearity enhances the polarization rotation of the focused beams during propagation. Furthermore, the anisotropy of optical nonlinearity causes the symmetry breaking of the polarization distribution. Further studies show that the C-point and L-line singularities of the lemon Poincaré beams do not change while propagating in free space or through the isotropic nonlinear Kerr medium. Interestingly, the anisotropic Kerr nonlinearity evokes the C-point singularity splitting, and the distortion and birth of L-line singularities. However, the total singularity index always remains the conservation within the light field, regardless of the optical nonlinearity.

Multi-stage heat release in lean combustion: Insights from coupled tangential stretching rate (TSR) and computational singular perturbation (CSP) analysis

There is a growing interest in leaner burning internal combustion engines as an enabler for higher thermodynamic efficiency. The extension of knock-limited compression ratio and the increase in specific heat ratio with lean combustion are key factors for boosting efficiency. Under lean burning conditions, there is emerging evidence that certain fuels exhibit unusual heat release characteristics. It has been reported that fuel/air mixtures undergo three-stage heat release or delayed high temperature heat release: starting with an initial low temperature heat release, similar to the one observed in two stage ignition, followed by an intermediate stage where thermal runaway is inhibited, and then advances to a relatively slow third stage of combustion. The focus of this study is to examine the conditions under which various fuels exhibit three stage ignition or delayed high temperature heat release. The auto-ignition of hydrocarbons/air mixtures is simulated in a closed adiabatic homogenous batch reactor where the charge is allowed to auto-ignite at constant volume vessel under predefined initial temperature and pressure. The simulations cover pressures of 10–60 bar, temperatures of 600 K–900 K, and fuel to air ratio from stoichiometry (equivalence ratio) of 0.3–1.0. Tangential stretching rate (TSR) and the computational singular perturbation Slow Importance Indices for temperature are used to identify important reactions contributing to the temperature growth rate at critical time instants of the auto-ignition process. Overall, three-stage ignition or delayed high temperature heat release is found to be present for most fuels under lean fuel/air mixtures, high pressures, and low temperature conditions. The radical termination reactions of H, OH, and HO2 during the high temperature heat release are leading factors for the distinct separation of heat release stages.

Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran

It is essential to study spatial distribution of Potentially Toxic Elements (PTEs) in surface soil, and separate PTEs geochemical background from their human sources, and also determine their correlation with other environmental factors in order to assess their impacts on humans, provide realistic environmental geochemical maps, and carry out soil management. For this purpose, this study was designed to investigate the surface soil in Ahvaz, Southwest of Iran. The applied methods were exploratory data analysis (EDA), including boxplot, Q-Q plot, probability plot (PP), concentration-area (C-A), number-size (N-S) fractal model, and singularity index (SI) model. The obtained results revealed that the fractal models led to a more realistic distinction among the geochemical population compared to the EDA methods such as probability plot. Furthermore, the C-A model was found to be more effective on the separation of subpopulation compared to the N-S and PP models. The studied elements exhibited a similar pattern implying that pollution is a function of geochemical dispersion regarding the surface soil in Ahvaz (Zn ≫ Pb > Cu > As). The studied metals-major elements plot also indicated that there was no meaningful relationship between Pb, Zn, Cu, and major elements in the study area. Plots of association of Pb, Zn, Cu, and As distinctly showed two general geogenic and anthropogenic populations. Moreover, the results of SI revealed that the highly contaminated area was consistent with the main defined hotspots and anthropogenic sources of elements as well as places affected by the contaminated area that have not been reported in previous studies. Furthermore, a combination of geochemical and geographical model comprising different statistical models was developed to more effectively separate geogenic from anthropogenic sources. Also, the geochemical background for the studied elements (Pb 180 mg/kg; Cu 200 mg/kg; Zn 90 mg/kg; As 65 mg/kg) was shown to be higher than the Iranian soil quality guideline with Pb, Zn, Cu, and As of 100, 80, 200, and 18 mg/kg, respectively.

Use of q-plate as a coupler.

A coupler is a device that allows interconversion of angular momentum of the optical beam among spin and orbital parts, without absorbing any angular momentum. In this paper, we demonstrate that a q-plate with q=1 can act as a coupler for any of the polarization distribution that can be represented by a point on a Poincare sphere (PS), or on a higher-order PS, or on a hybrid-order PS. A q-plate with any q-value can act as a coupler for polarization distribution that can be represented by an equatorial point on any of these spheres. It is also possible to find a q-plate with an appropriate q-value that can act as a creation or annihilation operator that raises or lowers the polarization singularity index. The q-plate being a coupler and made of half-wave plate segments, the singularity index change is concomitant with the helicity inversion of C-points. Interconversion of bright and dark C-points is possible by using a q-plate.

Application of Finite Difference Method in Multifractal Techniques to Identify Pb–Zn Anomalies: a Case Study from Khomein, Iran

Separation of geochemical anomalies from background is an important and essential issue in the exploration of mineral deposits. Statistical methods are used to separation of anomaly from background. Fractal/multifractal and singularity methods are very suitable for the separation of weak anomalies. For interpolation, various methods could be used such as most distance-weighting methods, kriging, spline interpolation, interpolating polynomials, and finite difference methods. This paper addressed application of multifractal techniques together with finite difference procedure to detect the anomalous regions. Fractal models such as concentration–area, spectrum–area, and singularity index model were selected to identify Pb–Zn anomalies. In this research, 170 samples from 33 boreholes were taken in the Haft-Savaran area and analyzed for determination of Pb–Zn anomaly. Singularity method was a suitable method for separating Pb and Zn anomalies. Also, the results showed that the geochemical anomaly has occurred along NE–SW trend and is consistent with the sulfide mineralization process and the faults of the region.

THE SCIENTIFIC AND TECHNICAL PUBLICATIONS TEXT AUTHORITICATION METHOD BASED ON LINGUSTICAL ANALYSIS OF LANGUAGE DIVERSITY COEFFICIENTS

Context. Authorization of the authorship of the text is a technique for determining the author of the text, when it is ambiguous who wrote it. It is useful when several people claim to be the authors of one publication or in cases where nobody claims to authorship of text content, for example, so-called trolls in social networks during an information warfare. The complexity of the problem of the author’s text, obviously, is exponentially higher, more likely authors. The presence of author’s text samples is also significant in advancing this problem. The attribution of the author’s text includes the following three problems:– author discovery of text from probable or expected authors group, where the author is always in a suspects group;– not identification of the author of a text author from a group of probable or expected authors, where the author may not be in a group of suspects;– assessment of the possibility of this text, written by the author or not.Therefore, the task of automatically determining the author of text content of scientific and technical direction is relevant and requires new (more perfect) approaches to its solution.Objective of the study is to develop a method for determining the author in Ukrainian texts based on the technology of lingometry. Method. Lingvometric method of algorithmic provision of content monitoring processes for solving the problem of automatic determination of the author of Ukrainian-language text content on the basis of technology of statistical analysis of linguistic diversity coefficients is developed. A decomposition of the method of determination of the author on the basis of analysis of such broadcasting factors as lexical diversity, degree (degree) of syntactic complexity, speech connectivity, singularity indexes and text concentrations is made. Also, author’s style parameters are analyzed as the number of words in a particular text, the total number of words in this text, the number of sentences, the number of prepositions, the number of conjunctions, the number of words with the frequency of 1, and the number of words with a frequency of 10 or more. The features of the developed is the adaptation of the morphological and syntactic analysis of lexical units to the features of the designs of Ukrainian-language words / texts. That is, in the analysis of linguistic units of the type of words, the affiliation with the part of speech and declarations within this part of the language was taken into account. To do this, an analysis of the flexion of these words was carried out for classification, the allocation of the basis for the formation of the corresponding alphabet-frequency dictionaries. The filling of these dictionaries was further taken into account in the subsequent steps of determining the authorship of the text as the calculation of parameters and coefficients of copyright broadcasting. For the individual style of a writer, it is precisely service (stop or reference) words that are indicative because they are not related to the topic and content of the publication.Results. A comparison of results on a plurality of 200 individual technical works of about 100 different authors over the period 2001–2017 has been made to determine whether the coefficients of the diversity of the text of these authors are different at different intervals.Conclusions. It has been found that for the chosen experimental base with over 200 works of the best results, the method of analysis of the article without initial obligatory information as annotations and keywords in various languages and the list of literature achieves the density criterion.

Perturbation-induced morphological transformations in vector-field singularities

In this paper, we report the morphological transformations of vector-field singularities (V-points) under symmetric and asymmetric perturbation using a diamond-shaped diffracting aperture. The number of null intensity points in the diffraction pattern depicts the magnitude of the polarization singularity index. Also, under symmetric perturbation, a higher-order V-point singularity disintegrates into|η|number of unit-index singularities following the index conservation rule. Asymmetric perturbations, realized by laterally shifting the aperture, lead to the formation of pairs of generic elliptic-field singularities (C-points) that are more fundamental and stable. In this process, the helicity and polarization singularity index are both conserved. This may find application in the context of singularimetry, where weak fields are measured by considering them as perturbations of unstable optical beams [Phys. Rev. Lett.109,183903(2012)PRLTAO0031-900710.1103/PhysRevLett.105.053904].

Multi-parameter Analysis of Local Singularity Mapping and Its Application to Identify Geochemical Anomalies in the Xishan Gold Deposit, North China

Identification of geochemical anomalies from geological background is of great significance in the exploration of complex mineralization systems. For a 2D problem, the window-based local singularity mapping has been used widely to identify the distribution patterns of geochemical data. However, the optimal window parameters for calculating the singularity index are hard to determine. Previous studies commonly use the default parameters when applying singularity mapping. In this study, window-based local singularity mapping was performed and improved by comprehensive analysis of multiple parameters to explore geochemical anomalies associated with gold mineralization in the Xishan deposit, North China, with the aim of revealing undiscovered mineralization. By using Au anomalies as an example, the parameters that may influence the result of window-based local singularity processing have been analyzed and discussed to improve the mapping result. The parameters include the average concentration calculation algorithm, the shape of the sliding windows, the window size increment and the number of windows. Success-rate curves and area under the success-rate curve have been used to assess the spatial correlation of the singularity map with the known mineral occurrences. While square sliding window is the most regularly used window shape, circular and elliptical windows can be alternative choices. We found that the directions of major axis of the ellipses parallel or quasi-parallel to geological strike fit the locations of ore deposits better if the mineralization system is controlled by regional faults. After taking the influence of different parameters into account, geochemical anomalies were successfully separated from background and have been enhanced compared to anomalies identified solely from concentration values. Singularity–quantile analysis has been applied to recognize and separate multiple geochemical anomaly populations based on the singularity map in frequency and spatial domain. While the Au concentration map shows quite scattered strong and weak geochemical anomalies, the linear regions of positive singularity resolved by singularity–quantile analysis coincide well with the location of regional faults and alteration zones, which might indicate footprints of ore-forming fluids. Based on the singularity maps of multi-elements (Au, Ag, Cu and Pb), we resolved two prospect areas of mineralization bounded by regional faults, hydrothermally altered rocks and lamprophyres with positive singularity, which warrant further investigation for undiscovered mineralization.

Fitting CAD data to scanned data with large deformation☆

Abstract Recently, largely deformed simulation models, such as car crash simulations, have been in high demand. To evaluate such models, it is necessary to use actual deformation results to compare and validate them. When measuring actual deformed objects, an X-ray CT is useful because it is non-destructive. However, matching undeformed CAD data and scanned deformation data is difficult. We propose a system for users to set control points or control lines on feature points and predict deformation using affine transformation with the moving least squares method. In the proposed method, undesirable distortions are reduced by evaluating scaling using singular value index and introducing offset control points. The deformed CAD data are obtained by matching predicted CAD data and a polygon mesh generated by deformed CT data. In addition, the surface elements of the generated deformed CAD data are evaluated for CAE.

Tool condition monitoring in milling using a force singularity analysis approach

Tool condition monitoring (TCM) is extremely important to ensure production efficiency and workpiece quality. It is crucial to extract and select suitable features from raw signals to improve the robustness and feasibility of TCM systems. This paper introduces a cutting force singularity analysis approach for TCM in milling, which correlates tool wear states with force waveform variations. The Holder exponents (HEs) were selected as the index of singularities. HEs are calculated by wavelet transform modulus maxima (WTMM). The raw signal is de-noised based on WTMM estimation, which can effectively preserve singularities compared with traditional filters. Fisher’s discriminant ratio (FDR) is employed to rank the discriminant capability of statistical features of HEs. It is found that means of HEs and quantities of singular points estimated from feed forces Fx show the strongest class-discriminant ability. Then, these features were chosen as training samples to propose a TCM approach based on the support vector machine (SVM). Experimental results indicate that this approach provides reliable and effective advice for tool change.

An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators

Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification.

Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States

An association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space. To quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois. In this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n = 71 901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n = 789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime. Fifteen variables, measured as a 5-year mean, were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019. Among the 71 901 census tracts examined across the continental United States, a median (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2 = 0.63; P < .001), even after accounting for violent crime and spatial structures. The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.

Comprehensive assessment of the health and physical development in martial arts athletes

Thirty three athletes’ health (average age – 16.4±1.0 years) were studied. Anthropometry and analysis of body composition, and load test were performed by the Hardware-software complex "Zdorovye-Express" (Russia). Spirometry, electrocardiography, assessment of physical performance as per Harvard step test, and ultrasound scan of abdominal organs, blood biochemical indicators were also performed. It is established, examined athletes have medium level of harmony of development and high level of physical fitness. Harvard step test data showed that the vast majority of athletes have an adequate level of physical performance (66.6%) with good recovery ability, and 33.4% - a very poor level of physical performance with a normal or slow recovery period after exercise. Assessing the cardiac singular index, the condition of the cardiovascular system in 82.6% surveyed was considered as optimal and in 17.3% examined as moderately increased. Spirometer indicators confirmed to the age norms. Ultrasound data of the abdominal organs showed the presence of disturbances in 24% of cases, while these disorders were manifested by diffuse liver echogenicity changes in 7% of cases and pathology of the gallbladder in 17% cases. The revealed changes in the health status of athletes require further research.

Identifying geochemical anomaly through spatially anisotropic singularity mapping: A case study from silver-gold deposit in Pangxidong district, SE China

The anisotropic singularity analysis, also referred to as Spatially Weighted Singularity Mapping (SWSM), was developed by means of multifractal modeling and spatial weighting. The SWSM extends the application of the Ordinary Singularity Mapping (OSM) technique, which is spatially isotropic, to the anisotropic case. In this paper, the SWSM method was demonstrated in a case study where the geochemical anomaly associated with the silver-gold mineralization was identified through mapping the spatially anisotropic singularity of the pathfinder elements including Ag, Au, Cu, Pb, and Zn, to in Pangxidong district, south of the Qinzhou-Hangzhou Metallogenic Belt, SE China. Firstly, using the stream sediment geochemical data and spatial weighting factor linked to NE-trending ore-controlling fault system, the local anisotropic singularity index values (i.e. ϵ-values) were estimated for each of the five elements. Then, Principal Component Analysis (PCA) was applied to the ϵ-values to obtain comprehensive geochemical anomaly of the multiple element (i.e. PC1 scores). It appeared that the comprehensive geochemical anomaly has a better prediction efficiency with the discovered silver-gold occurrences than the one derived from the OSM-PCA approach (PCA applied to singularity index values estimated by the OSM technique). The performance tests were assessed by using the receiver operating characteristics as well as prediction-area plot and concentration-area fractal analysis methods. Finally, the student's t-static method was employed to determine the reasonable threshold values separating the geochemical anomaly and background. The student's t-values indicate that spatial correlation between the locations of the known silver-gold mineral deposits and the geochemical anomaly derived by the SWSM-PCA approach was much stronger than that of the geochemical anomaly derived by the OSM-PCA method. The geochemical anomalies delineated by the SWSM-PCA approach were considered to be the favorable target areas for exploration of similar undiscovered silver-gold polymetallic deposits. This study has demonstrated that the SWSM approach can incorporate the spatial anisotropic information (e.g. direction and range of spatial variability), which exhibits geological controlling features, into the singularity analysis of geo-variables in various geo-spatial datasets such as geochemical, geophysical and remote sensing data.

Characterization of Deltaic Channel Morphodynamics From Imagery Time Series Using the Channelized Response Variance

River deltas are complex, dynamic systems whose channel networks evolve in response to internal and external forcings. To capture these changes, methods to extract and analyze deltaic morphodynamics automatically using available remotely sensed imagery and experimental observations are needed. Here, we apply a promising method for the automatic extraction of channel presence called RivaMap, on both synthetic and experimental data sets, to investigate the changes experienced by the system in response to five changes in forcings. RivaMap is an automated method to extract nonbinarized channel locations from imagery based on a singularity index that combines the multiscale first and second derivatives of the image intensity to favor the identification of curvilinear features and suppress edges. We quantify how the channelization varies by computing the channelized response variance (CRV), which we define as the variance of each pixel's singularity index response through time. We find that increasing magnitudes of sediment inflow (Qs) and water inflow (Qw) result in corresponding increases in the maximum CRV. We find that increasing the ratio of Qs to Qw results in increased number of channelized areas. We see that adding cohesion to the exposed sediment surface of the experimental delta results in decreased magnitude and decreased number of channelized areas in the CRV. Finally, by observing changes to the CRV over time, we are able to quantify the timescale of internal channel reorganization events as the experimental delta evolves under constant forcings.

Efficient Water Quality Prediction Using Supervised Machine Learning

Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.