International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Analyzing Behavioral and Population Dynamics of House Sparrows Using ARIMA, Classification, and Clustering Models Omkar Singh1, Amit Kumar Pandey2, Giridhar Balakrishnan3, Jagmohan Bal 4, 1HOD of MSc Data Science, 2 Assistant Professor, 3,4 PG Student (MSc Data Science), Thakur College of Science and
Commerce Thakur Village, Kandivali (East), Mumbai-400101, Maharashtra, India ------------------------------------------------------------------------***--------------------------------------------------------------------Abstract: Once a common sight in both urban and rural settings, house sparrows (Passer domesticus) have seen substantial population changes over time. Conservation and ecological balance depend on our ability to comprehend their population dynamics and behavioral adaptations. This work analyzes house sparrow populations across various geographic and temporal variations using machine learning approaches, such as clustering to identify important environmental parameters, classification for habitat-based population analysis, and ARIMA for time-series forecasting. Time-dependent population changes are evaluated by analyzing the dataset covering 2015–2025. While classification models like Random Forest are used to classify sparrow populations according to environmental circumstances, ARIMA is used to predict future population trends. Different population groupings and environmental factors affecting population dynamics can be identified with the use of clustering techniques such as K-Means and Hierarchical Clustering. The findings show that clustering identifies important patterns in sparrow habitats, classification models correctly identify population trends based on environmental parameters, and ARIMA predicts population changes. By helping policymakers create data-driven plans for preserving bird biodiversity, this research advances our understanding of house sparrow conservation efforts. Index Terms: House Sparrow Trends, Environmental Factors, Predictive Modeling, Time Series Forecasting, Avian Population Dynamics, Habitat Clustering, Geospatial Analysis, ARIMA Forecasting, Species Observation Data, K-Means Clustering, Random Forest Classification, Ecological Data Analysis, eBird Dataset Processing, Satellite Image-Based Analysis.
Introduction: Once a species that thrived in both urban and rural settings, house sparrows (Passer domesticus) have experienced sharp population declines in recent years. Urbanization, climate change, and habitat loss are among the primary causes of this reduction. While traditional ecological studies rely on field surveys and observational data, these approaches often lack scalability and predictive accuracy. Machine learning offers a data-driven approach to analyzing large-scale avian population data; however, research in this area remains limited, with most studies focusing on direct field observations rather than predictive modelling. Several key challenges exist in house sparrow population analysis. There is a limited application of artificial intelligence and machine learning models for studying long-term population dynamics. Additionally, the lack of integration of temporal trends makes it difficult to accurately forecast future sparrow populations. Data-driven methods for classifying habitatbased population fluctuations are underutilized, and there is a need for a comprehensive examination of environmental factors influencing sparrow distribution. To address these gaps, this study applies machine learning techniques, including Random Forest for habitat-based classification, ARIMA for time-series forecasting, and K-Means clustering to identify environmental influences on sparrow populations. The dataset, covering the years 2015–2025, enables an in-depth analysis of population trends across different geographical and temporal scales. The primary objectives of this research are: developing a predictive model using ARIMA to anticipate future sparrow population trends; classifying populations based on habitat conditions using machine learning algorithms, and utilizing © 2025, IRJET
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