In this study, we investigated the effectiveness of machine learning techniques in predicting water potability based on water quality attributes. Initially, we applied seven classification-based methods directly to the original dataset, yielding varying accuracy scores. Notably, the Support Vector Machine (SVM) achieved the highest accuracy of 69%, while other methods such as XGBoost, k-Nearest Neighbors, Gaussian Naive Bayes, and Random Forest demonstrated competitive performance with scores ranging from 62% to 68%. Subsequently, we employed Principal Component Analysis (PCA) to reduce the dataset’s dimensionality to six principal components, followed by reapplication of the machine learning techniques. The results showed an increase in accuracy across all classifiers, increasing to nearly 100%. This study provides insights into the impact of dimensionality reduction on predictive accuracy and underscores the importance of selecting appropriate techniques for water potability prediction.

This paper delves into the intricate relationship between changes in Magnetic inclination and declination at specific geographical locations and the navigational decisions of migratory birds. Leveraging a dataset sourced from a prominent bird path tracking web resource, encompassing six distinct bird species’ migratory trajectories, latitudes, longitudes, and observation timestamps, we meticulously analyzed the interplay between these avian movements and corresponding alterations in Magnetic inclination and declination. Employing a circular von Mises distribution assumption for the latitude and longitude distributions within each subdivision, we introduced a pioneering circular-circular regression model, accounting for von Mises error, to scrutinize our hypothesis. Our findings, predominantly supported by hypothesis tests conducted through circular-circular regression analysis, underscore the profound influence of Magnetic inclination and declination shifts on the dynamic adjustments observed in bird migration paths. Moreover, our meticulous examination revealed a consistent adherence to von Mises distribution across all bird directions. Notably, we unearthed compelling correlations between specific bird species, such as the Black Crowned Night Heron and Brown Pelican, exhibiting a noteworthy negative correlation with Magnetic inclination and a contrasting positive correlation with Magnetic declination. Similarly, the Pacific loon demonstrated a distinct negative correlation with Magnetic inclination and a positive association with Magnetic declination. Conversely, other avian counterparts showcased positive correlations with both Magnetic declination and inclination, further elucidating the nuanced dynamics between avian navigation and the Earth’s magnetic field parameters.

Studying meteoroid impact patterns on planetary surfaces is critical for understanding surface dynamics and selecting safe landing sites. Mercury, one of the least explored rocky planets, presents unique challenges due to its extreme temperatures and the angular nature of its surface data. Traditional linear statistical methods are often inadequate for analyzing such directional data. This study introduces a novel approach using directional data analysis techniques to interpret the cyclical nature of meteoroid impact locations on Mercury. We employed Watson’s test, Bayesian Information Criterion scores, and a mixture of von Mises–Fisher distributions to model the distribution of impact craters and solar elevations on Mercury’s surface. Our findings indicate that while location parameters adhere to the von Mises distribution, solar elevations do not exhibit directional distribution characteristics. Additionally, by filtering datasets based on temperature thresholds and crater diameters, we pinpointed areas with a high probability of providing suitable landing surfaces. These insights enhance our understanding of the surface conditions on Mercury and provide valuable groundwork for future exploration missions.

Classification of galaxies, stars, and quasars using spectral data is fundamental to astronomy, but often relies heavily on redshift. This study evaluates the performance of 10 machine learning algorithms on SDSS data to classify these objects, with a particular focus on scenarios where redshift information is unavailable. Leveraging features such as “z,” “u,” “g,” “r,” “i,” and redshift, we assess the accuracy of various algorithms, including XGBoost, Random Forest, and recurrent neural networks (RNNs). Our analysis demonstrates the superior accuracy of the Random Forest classifier when redshift is included. The feature importance analysis reveals that “redshift” is the most critical feature, contributing 64.7% to the classification accuracy, followed by the “z” band (10.0%) and the “g” band (7.95%). However, even in the absence of redshift, XGBoost, Random Forest, and RNNs exhibit promising results, indicating the potential of photometric data for accurate classification. We systematically compare classification outcomes with and without redshift, revealing the relative importance of different features and identifying the most robust classifiers for redshift‐limited scenarios. This research not only highlights the power of machine learning for astronomical classification but also provides a framework for reliable classification when redshift data is lacking. By uncovering the distinguishing spectral characteristics of galaxies, stars, and quasars that are independent of redshift, we open new avenues for efficient and accurate classification in large‐scale photometric surveys and the study of faint, high‐redshift objects.

This paper takes a probabilistic approach to validate novel empirical models and the directional distributional similarities of nearby earthquake counts related to a typical volcano with its eruption duration. Considering the datasets on volcanic eruptions and earthquakes, focusing mainly on earthquakes within a 100-kilometer radius and within a three-year time frame of the volcano eruption. An empirical probabilistic models for the same; statistical model validation tests favor the proposed models is proposed. In addition, a novel directional statistical approach to characterize the interconnection and distributional similarities of volcanic eruptions and earthquakes near volcanoes, utilizing the directional nature of the datasets. The project and partition the volcanic eruption and earthquake data to assess its directional distribution is shown. The analysis demonstrated that the data adhered to a Von Mises distribution and unsupervised equal partition revealed for both datasets, highlighting the interconnected nature of volcanic eruptions and earthquakes. Also, Von Mises-Fisher distribution fit test is applied to this work; the analysis produced partition results that closely aligned with the partitions obtained through the 2D projection. This congruence emphasizes the robustness of the findings in a spherical context. the proposed empirical models and conclusions on distributional similarities may provide insights into the underlying mechanisms connecting these geological phenomena.

The study encapsulates the investigation into the spherical distributional characteristics of parameters relevant to Gamma-Ray Bursts (GRBs), mainly focusing on their Galactic coordinates. The study utilized a mixture of von Mises Fisher spherical distributions to model the spatial distribution of GRBs in both BATSE and FERMI catalogs. Optimal numbers of mixture components were determined for different subsets of GRBs, including Long and Short GRBs. For the BATSE catalog, it turns out that a mixture of two spherical distributions provides a good fit for the whole data set and long and short GRBs. On the other hand, for the FERMI catalog, it turns out that a mixture of three spherical distributions provides a good fit for the whole data set, and a mixture of four distributions is adequate for both long and short GRBs. Additionally, an assessment was made to determine if the location parameter of GRBs follows any spherical distribution. Our flexible directional statistical modeling framework reveals that GRBs exhibit a non-uniform distribution on the celestial sphere, as evidenced by rejecting the null hypothesis of uniform distribution on a sphere using the Watson test. Our analysis statistically inquires the long-held assumption of their isotropic spread, especially in 2D projected spatial distributions of GRBs, suggesting that these cosmic events might not be uniformly scattered across the celestial sphere. The observed clumping of GRBs hints at the underlying cosmic scaffolding—the large-scale distribution of matter and star formation. Our results statistically asserts the explanation that the intrinsic GRB formation rate is typically tied to cosmic star formation rates with a delay time distribution, leading to a non-uniform rate as a function of redshift, demanding more nuanced calculations. However, this finding needs to consider potential biases introduced by the Milky Way’s obscuration and our heliocentric perspective.