Monitoring Information Research Articles

Contractor-oriented optimization for wind farm maintenance decision-making considering partial monitoring information and degraded working efficiency

Maintenance strategies play a crucial role in the development and success of wind farm management, as they have a significant impact on the profitability of wind generation. The current maintenance decision-making process focuses mostly on the wind farm owner, while ignoring the benefits from the perspective of maintenance contractor. To bridge this gap, a novel contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms to maximize profits for the maintenance contractor. Compared to the reported works, two main contributions are made. First, it is the first time that the maintenance strategy of a wind farm is designed from the perspective of the maintenance contractor, with emphasis on an accurate profit assessment. For this purpose, by utilizing remaining useful life (RUL) information from monitorable and non-monitorable components, a degradation-related efficiency model is constructed to quantify the loss of turbine efficiency due to degraded components and to incorporate it into the maintenance decision-making process. Secondly, various identifiable failure modes of the turbine components are investigated and simulated, as the contractor is more concerned about the maintenance costs under different failure modes. A case study using the dataset collected from real wind farms is provided to demonstrate and validate the proposed maintenance optimization method. The results indicate the proposed method can contribute to the maximum profits of the contractor while satisfying the requirements of the wind farm owner.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

The EL-BIOS Earth Observation Data Cube for Supporting Biodiversity Monitoring in Greece

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

DEVELOPMENT OF WATER QUALITY MONITORING SYSTEM IN SHRIMP PONDS BASED ON INTERNET OF THINGS (IOT)

This type of research is research and development (R&D) which uses the waterfall development model. The aim of this research is to produce a water quality monitoring system in shrimp ponds based on the Internet of Things (IoT) and to find out the results of testing the development of the system using the ISO 25010 standard. This development involves the application of ESP32, Relay, Toa Siren, DS18b20 Temperature Sensor, pH Sensor Module 4502C, Water Salinity and Turbidity Sensor which allows users to get real-time pond water quality monitoring information via the Telegram application. The test results for the reliability aspect have met the fault tolerance sub-characteristic standards because the percentage of measurement error obtained for the temperature sensor was 0.10%, pH 0.54%, salinity 1.19%, and turbidity 1.00%, still within the permitted tolerance limits. Functionality suitability testing shows that this tool obtains a percentage of 100%, indicating that the system is suitable for use. This tool also meets the portability aspect because it can be used on various devices with different operating systems. Apart from that, the usability evaluation results from user responses are in the very good category with an average of >4.2.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine.

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

First Report of Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae) in Oklahoma.

The corn leafhopper, Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae), is an invasive insect that can cause damage to maize (Zea mays L.) in two ways: by direct feeding and the transmission of several plant pathogens. Dalbulus maidis is an invasive and serious economic pest of maize that has spread from its center of origin in Mexico to the southernmost parts of the United States. Prior to 2024, corn leafhoppers had not been documented in Oklahoma, and their spread northward toward the United States corn belt is of significant concern. Here, we provide the first reports of the insect in maize in several Oklahoma counties. Insect specimens were collected at various commercial and experimental field sites by Oklahoma State University research and extension personnel. The identity of the insect species was validated through morphological and molecular taxonomy. The presence records for the corn leafhopper presented here provide valuable information for future monitoring and management efforts of this economically important pest and disease.

Cloud base height variability observed using a Laser-Based Ceilometer over a tropical station Pune, India

ABSTRACT Clouds play a pivotal role in determining Earth’s climate, impacting several atmospheric processes influencing the weather. The study explores the temporal variations in cloud base height (CBH) over Pune, a tropical station, from 2017 to 2023 using a Laser-Based Ceilometer with a range of 0–7500 m. Our analysis shows seasonal trends in CBH, with the monsoon season exhibiting unique characteristics showing the lowest average CBH and minimal diurnal changes during the study period. Conversely, the pre-monsoon, post-monsoon, and winter season show a gradual rise of CBH in the morning, maintaining higher levels until late afternoon, followed by a decline in the evening. Analysis of the diurnal frequency of cloud occurrence at 500 m intervals reveals the monsoon season’s dominance in the 500 m to 1000 m layer. Throughout the year, low cloud bases (CBH <2000 m) are most frequent, outpacing the frequency of mid and high cloud bases. High cloud base (7500 < CBH > 6000 m) is the least common, showing a semi-diurnal pattern in winter with peaks around noon and midnight. These findings not only enhance our understanding of Pune’s atmospheric behaviour but also offer valuable information for weather prediction, climate modelling, and environmental monitoring.

A novel approach to aeroengine performance diagnosis based on physical model coupling data-driven using low-rank multimodal fusion method

Aeroengine health assessment plays a pivotal role in ensuring flight safety and reliability. Traditionally, this process involves diagnosing the performance of the aeroengine gas path. However, owing to the intricacies of operating conditions, non-linear performance, and the interplay of gas path performance fault characteristics, determining the aeroengine health condition directly from engine monitoring information poses a significant challenge, particularly in cases of insufficient sensor data. To address these challenges, a novel digital twin method for aeroengine performance diagnosis has been proposed. This method integrates data-driven and performance models, employing a low-rank multimodal fusion approach. By digitizing the physical system or process through mathematical models and simulation technology, this approach presents distinct advantages compared to previous methods relying solely on models or data. At the aeroengine component level, an adaptive model was implemented, and the data-driven model was constructed using flight data. Gas path fault classification employed support vector machines. The engine digital twin was established through low-order multimodal fusion. Results indicate that the proposed method attains excellent diagnostic accuracy under both steady and transient conditions. It can be harnessed to enhance engine performance monitoring and evaluation, thereby improving the reliability, availability, and efficiency of the engine.

Can satellite InSAR innovate the way of large landslide early warning?

Predicting landslide failure times is an essential component in landslide risk management. Although in-situ sensor-supported landslide early warning systems are still predominantly used, their high cost makes it impractical to monitor all the landslides, thereby posing a major challenge for the effective landslide risk management. Hence, this study investigated this problem from an earth observation perspective and proposed a probabilistic landslide failure time prediction framework integrating Interferometric Synthetic Aperture Radar (InSAR) monitoring information. Accordingly, 30 historical landslides that occurred between 2016 and 2021 in central and western China were collected to evaluate the feasibility of the aforementioned framework. Based on the landslide dataset, the performance of the satellite InSAR technology for landslide failure time prediction is evaluated systematically from an application perspective. It was evident that eleven landslides (36.67 %) were captured by InSAR with accelerated deformation signals before failure, and monitoring data from eight (26.67 %) of them provided enough information for their failure time prediction. Further, a probabilistic method integrating the conventional inverse velocity model and sequential Bayesian updating was proposed to dynamically predict the most likely failure time and related confidence interval. Case studies showed that the proposed method could successfully predict the failure time of the eight landslides, thus demonstrating the feasibility of the framework. Although the current long revisit period of satellites constrains their performance practically, this problem can be solved by advancements in future satellite missions. Thus, we believe that the InSAR era is imminent and will bring substantial values for large landslide early warning.

Azaspiracid-59 accumulation and transformation in mussels (Mytilus edulis) after feeding with Azadinium poporum (Dinophyceae)

Azaspiracid-59 (AZA-59) was detected in plankton in coastal waters of the Pacific Northwest USA. Given that bivalves metabolize and transform accumulated phycotoxins, a strain of Azadinium poporum isolated from the coast of Washington State that is a known producer of AZA-59 was used in a controlled feeding experiment with mussels (Mytilus edulis) to assess AZA-59 accumulation rates and transformation into shellfish metabolites. Mussels started feeding immediately after the addition of A. poporum. Mussels were generally healthy during the entire experimental exposure of 18 days with prevailingly high rates of clearance (ca 100 mL per mussel and hour) and ingestion. Mussel were extracted after different exposure times and were analyzed by liquid chromatography coupled with low- and high-resolution mass spectrometry. In the course of the experiment a number of putative AZA-59 metabolites were detected including hydroxyl and carboxy analogs that corresponded with previously reported mussel metabolites of AZA-1. A significant formation of 3-OH fatty acid acyl esters relative to free AZAs was observed through the time course of the study, with numerous fatty acid ester variants of AZA-59 confirmed. These results illustrate the potential for metabolism of AZA59 in shellfish and provide important information for local AZA monitoring and toxicity testing along the Northern Pacific US coast.

Maximizing similarity: using correlation coefficients to calibrate kinetic parameters in population balance models

Crystallization plays a crucial role as a separation and purification technique, particularly in the chemical and pharmaceutical industries. By adjusting process parameters, the productivity, product quality, and efficiency of downstream processes can be improved. In complex processes, model-based design becomes invaluable. Population Balance Models (PBMs) have successfully aided the chemical industry in achieving more effective production processes for decades. These models can utilize various input data sources to identify the dominant mechanisms and calibrate model parameters. While inline particle monitoring tools serve as excellent qualitative descriptors, challenges arise from experimentation and data interpretation, hindering their direct application in the kinetic parameter estimation of PBMs. In this study, we present a novel approach that utilizes information from inline particle monitoring tools for the kinetic parameter estimation of PBMs, bypassing the associated obstacles. Our pioneering approach relies on offline product size data and the correlation-based utilization of inline particle monitoring information. The paper compares this novel strategy with two parameter estimation techniques: the classical method using solute concentration and product size data and a somewhat naïve approach, which assumes that the inline particle monitoring data can directly be compared with the simulations. The prediction capabilities are evaluated through two in-silico case studies. The results indicate that the precision and predictive capability of the correlation-based technique are comparable to the classical approach, both for noisy data and for a system undergoing significant agglomeration and deagglomeration. The use of Pearson’s correlation coefficient yields the best results in the novel cases. These findings in in-silico datasets provide a foundation and motivation for the practical application of this idea, unleashing the so-far hidden model development potential of such measurements.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.

WITHDRAWN: Design of Group Key Agreement Method Based on Elliptic Curve Cryptography in New Energy Extensive Access Security Protection

Aiming at the security problem of power system communication network under the condition of extensive access of new energy, we design the group key agreement method based on elliptic curve cryptography in the security protection of new energy extensive access. Design the architecture of power system security protection network under the condition of extensive access to new energy. The network architecture adopts wireless communication technology to realize the transmission of new energy security monitoring information. For the data confidentiality problem in the wire communication technology, the group key agreement method based on elliptic curve cryptography is proposed, and the group key agreement model is constructed under the set conditions, and the group key is generated through the processes of main parameter selection, group composite public key distribution, group key agreement protocol design, group key update protocol for node joining event, and group key update protocol for members departure event, etc. The group key is utilized for the communication. The experimental results show that this method can generate the key with high randomness and excellent key sensitivity, which can effectively resist the attacks of character frequency statistics and information entropy and guarantee the security of communication information.

Using an information and analytical system to assess the health status of the personnel of the Vostochny Space Center

The Vostochny Space Center is a new facility of the Russian space industry, which began launching space technology in 2016. In the normal operation of the cosmodrome, the impact of rocket fuel components on personnel, taking into account the data of sanitary and hygienic monitoring, is not expected, however, the influence of a chemical factor is possible in cases of emergency situations. The purpose of the study is a hygienic assessment of the impact of harmful and dangerous factors of the production environment on the health of the personnel of the Vostochny cosmodrome using an information and analytical system of sanitary and hygienic monitoring. The study was based on the classical methodological basis of a comprehensive sanitary and epidemiological assessment of the production environment. The analysis showed the absence of differences in the levels and dynamics of morbidity by the main classes of the disease, the absence of professionally determined pathology, which indicates the sufficiency of the complex of organizational, technical and sanitary measures implemented during the creation of the Vostochny Space Center. Thus, the introduction of information and analytical approaches to the organization and implementation of monitoring studies makes it possible to obtain the necessary comprehensive information, timely and fully record the impact of negative factors of the production environment on employees, and perform a comprehensive assessment of the health status of the personnel of the Vostochny cosmodrome.

Exploring the interplay between phytoplankton taxonomy, pigments, and optical properties in Ratnagiri coastal waters using a multi-parameter approach

This study presents a comprehensive analysis of phytoplankton diversity and optical properties in the coastal waters of Ratnagiri, India. Ratnagiri, located on the southern coast of Maharashtra, is one of the major centre for fishing activities in India as well as a developing hub for industrial activities. Given its ecological significance and the pressures it faces, the coastal waters of Ratnagiri present a valuable case for studying phytoplankton dynamics and their light absorption properties. We integrated parameters like phytoplankton taxonomy, pigment composition, Coloured Dissolved Organic Matter (CDOM), and phytoplankton absorption characteristics to give a comprehensive view of the phytoplankton community and its optical environment. Phytoplankton samples from Ratnagiri were analyzed with microscopy and high-performance liquid chromatography (HPLC) for taxonomy and pigment profiling, while CDOM and absorption properties were measured using spectrophotometry. Our findings reveal more or less spatial uniformity in phytoplankton composition (Pielou's Evenness Index (J’) ranged between 0.84 and 0.9), with significant diversity (Shannon Diversity Index (H’) ranged from 3.7 to 4.19) within the phytoplankton community. Pigment analysis revealed key marker pigments associated with various phytoplankton classes including bacillariophytes (diatoms), chlorophytes, cyanophytes, haptophytes and cryptophytes, providing insights into community structure. Concurrently, CDOM absorption coefficient (aCDOM (440)) that ranged between 0.08 and 0.3 m−1 indicated varying levels of dissolved organic matter, influencing light absorption and availability within the water column. The phytoplankton absorption data highlighted the specific wavelengths most affected by phytoplankton presence, notably those contributed by diatoms and cyanophytes, providing insights into the optical properties of Ratnagiri's coastal waters. This integrative approach underscores the importance of considering multiple parameters to accurately assess phytoplankton dynamics and their impact on the aquatic environment. The results contribute to a better understanding of the ecological processes governing coastal waters in Ratnagiri and offer valuable information for future monitoring and management efforts.

Impact of Digital Health Interventions on Birth Equity: A Review.

The rise in smartphone utilization and technology uptake has popularized digital health interventions as a means of supporting healthy pregnancies and optimizing maternal and child health. Digital health interventions include several modalities, such as telemedicine, remote patient monitoring, smartphone applications, web-based interventions, wearables, and health information technology. However, the impact of these interventions on improving maternal and infant health outcomes by race and socioeconomic status to achieve birth equity is unknown. This review summarizes current literature on the impact of digital health interventions on the outcomes of communities of color and lower socioeconomic status in the United States. We demonstrate there is emerging evidence of the impact of digital health interventions on maternal health outcomes, particularly for telemedicine, but evidence specifically focused on assessing outcomes by race and ethnicity and for other modalities, like mHealth apps or wearables, is limited. Digital health interventions may play a part in birth equity initiatives, but should not be considered a standalone solution, and instead should be integrated into other existing efforts to achieve birth equity, like diversifying the clinician workforce, expanding access to high-quality prenatal and postpartum care, or delivering respectful maternity care.

Analyze, monitoring and testing of computer information systems

The high importance and criticality of the efficiency and reliability of the functioning of modern computer information systems require efforts to ensure them, which are no less, than the efforts to develop the systems themselves. Offline and online monitoring and testing of computer information systems, which are often used to ensure efficiency and reliability of functioning, involve the development and implementation of appropriate hardware, software and information tools, as part of automated technical diagnostic systems. Features of both the computer information systems themselves and the means of monitoring and testing are their network distribution, autonomy and at the same time cooperation of all their components. Such features appear already in the system-functional presented systems and impose requirements for a special analysis of all their properties and functions, in that case also energy ones. The proposed work is focused on the implementation of energy-behavioral monitoring and testing of computer information systems, defines a formal presentation of a special analysis of the relevant hardware and software tools and is based on asynchronous-event models of extended Petri nets and behavioral experiments. This representation is based on models of energy-behavioral specification and testing, parallel, associative-evolutionary methods of monitoring and testing with the accumulation of blanks and results that differ in special features. The proposed presentation makes it possible to create a new system energy-behavioral technology for monitoring and testing computer information systems.

Optimising the review of electronic monitoring information for management of commercial fisheries

Electronic monitoring (EM) systems incorporating cameras and other devices can collect a broad range of data to support fisheries management. We reviewed the data collection capabilities of EM and considered approaches to increasing efficiency, including cost effectiveness, of EM review. EM can provide information on catch, effort, catch handling, bycatch mitigation, fishing gear and operational data, which are relevant for fisheries management including by Regional Fisheries Management Organisations (RFMOs). Methods to increase efficiency and decrease costs of EM review apply from the programme design phase, through data collection and review. At review, costs may be reduced by sampling imagery optimally to meet monitoring objectives. Considering RFMOs as users of EM-collected information, we applied EMoptim, an open-source simulation model developed in R that estimates the amount of EM review necessary to meet one or more user-specified monitoring objectives. EMoptim uses stratification to increase review efficiency and incorporates a function to explore review costs against the monitoring objectives set. We evaluated the amount of EM review needed to estimate catch with specified precision, using fishery data available from the Western and Central Pacific Fisheries Commission. Model outputs show that EM review requirements increase as catch frequency decreases, dispersion of catch events increases, and when more precise catch estimates are required. Geographical stratification reduced the amount of review required for more commonly caught species and when catch events were focused in a limited area. Optimising review rates across multiple monitoring objectives was most effective for more commonly caught species. We highlight opportunities for future use and development of this prototype modelling package.

Seasonal variation of home range size and habitat selection of elephants in the Kabompo landscape of Zambia

BackgroundMovements of large herbivores and habitat utilization provide critical information for ecological monitoring and conservation. However, such geospatial information rarely exists, especially in sub-Saharan Africa. This study aims to provide insight on seasonal variation of home range size and habitat utilization by African savanna elephants (Loxodonta africana) in Zambia’s Kabompo Landscape.MethodsTwo male sub-adult elephants (estimated age 10–15 years) were collared and monitored between March 2018 and December 2021 using the Geographic Position System (GPS) tracking devices. We used Minimum Convex Polygon (MCP), which uses the outermost points, to arrive at the largest home range of a study subject and the Kernel Density Estimator (KDE), which captures the intensity use of various parts of the home range. Distance from the river, elevation, slope, land cover type, fire intensity and Normalized Difference Vegetation Index (NDVI) were assessed against habitat selection by elephants using multinomial logistic regression.ResultsThe findings showed that the movements of the elephants were largely confined within the West Lunga National Park. The wet season had the largest home range (1720.8 km2) dominated by grassland, while the cold dry season had the smallest area of 931.8 km2. Elevation and land cover types had significant (p-value < 0.05) influence on home range across all the seasons. Other factors also had positive and negative impacts on the home range selection depending on the season.ConclusionsThe insights from this study can be exemplary relevant to conservation planning through monitoring seasonal movement of elephants.