Efficient Resource Management Research Articles

Adoption of Circular Economy in Sustainable Agriculture Practices in Magelang District: Influence on Productivity and Farmer Welfare

Objective: The objective of this study is to investigate the adoption of circular economy principles in sustainable agriculture practices in Magelang district, focusing on their impact on agricultural productivity and farmer welfare, with the aim of assessing how circular economy practices contribute to sustainable agriculture by improving resource efficiency, enhancing productivity, and promoting farmer welfare in Magelang district. Theoretical Framework: The theoretical framework of this study is grounded in the priciples of the circular economy, which emphasize maximizing resource efficiency, minimizing waste, and fostering environmental regeneration. In agriculture, these principles align with sustainable practices by promoting the reuse of organic materials, efficient resource management, and the reduction of chemical inputs to maintain soil fertility and ecosystem balance. By integrating circular economy principles with sustainable agricultural practices, this framework aims to enhance productivity, reduce costs, and improve farmers’ welfare while ensuring long-term environmental resilience. Method: Using a quantitative approach that includes surveys of 100 farmers and in-depth interviews, this research evaluates the implementation of CE principles, such as composting organic waste, rainwater harvesting, and crop rotation. Results and Discussion: The findings indicate that approximately 60% of farmers have adopted at least one CE practice, leading to a productivity increase of up to 25% compared to conventional methods. Additionally, CE adoption has proven to improve income stability and farmer welfare. However, barriers such as limited access to technology and funding reduce broader adoption of sustainable practices. Research Implications: This study recommends government and institutional support through training, technology access, and financial incentives to accelerate CE adoption. It provides actionable reccomendations for policymakers and stakeholders to support the broader adoption of circular economy practices in agriculture. Originality/Value: The originality of this research lies in its focus on the adoption of circular economy principles within the specific context of sustainable agriculture in Magelang District, an area with unique agricultural challenges and potential. This research combines empirical data on productivity and welfare impacts with qualitative insights into local adoption barriers and drivers. By integrating these aspects, the study provides a novel perspective on implementing sustainable agricultural practices tailored to the region’s socioeconomic and environmental conditions.

Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces

Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification.

A Roadmap for Integrating Sustainability into Software Engineering Education

The world faces escalating crises: record-breaking temperatures, widespread fires, severe flooding, increased oceanic microplastics, and unequal resource distribution. Academia introduces courses around sustainability to meet the new demand, but software engineering education lags behind. While software systems contribute to environmental issues through high energy consumption, they also hold the potential for solutions, such as more efficient and equitable resource management. Yet, sustainability remains a low priority for many businesses, including those in the digital sector. Business as usual is no longer viable. A transformational change in software engineering education is urgently needed. We must move beyond traditional curriculum models and fully integrate sustainability into every aspect of software development. By embedding sustainability as a core competency, we can equip future engineers not only to minimise harm but also to innovate solutions that drive positive, sustainable change. Only with such a shift can software engineering education meet the demands of a world in crisis and prepare students to lead the next generation of sustainable technology. This paper discusses a set of challenges and proposes a customisable education roadmap for integrating sustainability into the software engineering curricula. These challenges reflect our perspective on key considerations, stemming from regular, intensive discussions in regular workshops among the authors and the community, as well as our extensive research and teaching experience in the field.

Water utilization practices and wastage at household level: A case study of Lahore City

AbstractThe current research places its emphasis on addressing the issue of water shortage, which, if left unaddressed, could hinder sustainable development in the broader Punjab region, with a particular focus on the city of Lahore. In pursuit of our research objectives, we conducted a comprehensive survey that involved 600 households within Lahore. Our research methodology included the usage of both quantitative and qualitative analysis which included interviews and snowball sampling. Our findings indicate a clear and direct correlation between water shortage and patterns of water utilization and wastage. The absence of sustainable strategies to ensure adequate water supply, efficient resource management, and universal sanitation poses a substantial threat to our future. To address water shortages, it is crucial to encourage the use of water‐saving appliances and fixtures like toilets, low‐flow faucets, and showerheads. The purpose of these measures is to conserve water and align it with sustainable development goals by encouraging resource utilization that is efficient, minimizing environmental impact. By implementing these solutions, both households and border areas can benefit from significant water‐saving practices. Consequently, we recommend that the government implement targeted programs aimed at raising awareness among individuals about responsible water usage and reducing wasteful practices within households.

USE OF GEO-INFORMATION SYSTEMS FOR GROUNDWATER MONITORING

Abstract . The article is devoted to the study of the possibilities of using geographic information systems (GIS) for monitoring the state of groundwater, which is important for ensuring environmental safety and sustainable management of water resources. Groundwater is an important source of fresh water for the population, industry and agriculture, but it is vulnerable to various pollutions caused by both natural and anthropogenic factors. Therefore, the implementation of reliable monitoring systems that allow timely detection, assessment and forecasting of changes in water quality is necessary to prevent the deterioration of the ecological situation and prevent the spread of pollution. Geographic information systems (GIS ) offer significant opportunities for the collection and integration of large volumes of spatial data, including satellite images, remote sensing results, automated sensor data, and field observations. GIS allow creating multi-layered maps reflecting the ecological state of underground aquifers, modeling pollution processes and predicting their possible impact on nearby ecosystems and water sources. Thanks to the use of GIS, it is possible to combine data from different sources in a single interactive system, which simplifies analysis and provides the ability to quickly respond to any detected deviations. The article examines in detail the key methods used within GIS for groundwater monitoring, including satellite sounding to analyze changes in the earth's surface, spectral indices to assess the state of vegetation and soil salinity, and radar methods to determine soil electrical conductivity. Particular attention is paid to the application of three-dimensional modeling, which allows visualization of the spread of pollution in underground aquifers, which facilitates decision-making regarding the optimal location of monitoring stations and the design of protective structures. In combination with automated sensors recording water parameters in real time, GIS provides continuous monitoring of the physico-chemical indicators of groundwater and allows for prompt assessment of their condition. The article also analyzes the possibilities of using predictive modeling in GIS to assess the further spread of pollution and develop environmental risk management measures. It is described how mathematical models in GIS can help predict the impact of anthropogenic and natural factors on groundwater, which ensures the development of scientifically based solutions to reduce pollution and maintain water quality at a safe level. The implementation of GIS in groundwater monitoring is an important step towards increasing the efficiency of water resources management, ensuring their protection from pollution and preserving the ecological stability of the regions.

Application of the CMIP6 Approach for Determination of Climate Change Impact on Inflow to Malampuzha Reservoir, India

Optimal operation of a reservoir is important for the efficient planning and management of water resources. Inflow estimation into the reservoir is the basic information needed by a policy maker for the optimal planning of the reservoir operation. Climate change has become a major cause for the changes in the hydrological phenomena. An attempt was made to analyse the impact of climate change on Malampuzha reservoir inflow for the future using recent Shared Socioeconomic Pathways (SSP) scenarios. Two SSP scenarios, SSP 245 and SSP 585 were considered for the evaluation of changes in the meteorological variables for the future period. Rainfall-runoff modelling of the catchment area of the Malampuzha reservoir was done with IHACRES model for the period 2003-2022. The model performance was accepted in calibration (2003-2016) and validation (2017-2022) periods with R2 of 0.90 and 0.91 and RMSE 2.63 and 4.53 m3/s respectively. The outputs of SSP245 and SSP585 scenarios were given to the developed runoff model to estimate the future inflow volume into Malampuzha reservoir for the period of 2023 to 2042. From the results, the change in monthly temperature varies from -0.88oC to 3.3oC and change in monthly precipitation varies from -0.64% to 1.73% which indicates a decrease in the inflow of water into the reservoir for future period by 2.6% and 9.3% than the base period (2003– 2022) for SSP245 and SSP585 scenarios, respectively. The results of the study implies that better planning of the reservoir operation is needed for meeting the future demands.

Efesiensi Kepemimpinan Pendidikan Dalam Optimalisasi Sumber Daya Sekolah

The efficiency of educational leadership plays an important role in optimizing school resources to achieve quality education goals. This study aims to analyze the definition of effective and efficient leadership, the factors that affect the success of leaders in achieving goals, and the benefits of implementing efficient leadership, as well as effective leadership strategies in managing human and material resources in the school environment. The research method used is a qualitative approach with data collection techniques through literature studies. The results of the study show that leadership based on collaboration, transparency, and participatory decision-making has a significant impact on improving the efficiency of school resource management. In addition, the use of information and communication technology (ICT) has been proven to accelerate the administrative process and the appropriate allocation of resources. The study concludes that educational leaders who are able to integrate innovation and adaptive managerial approaches can improve school operational effectiveness while creating a productive learning environment

Development of an Autonomous Drone-Based Irrigation Decision Support System Utilizing Image Processing and Machine Learning Techniques

Efficient management of water resources is essential for sustaining the global food supply amidst growing populations and climate change. Traditional irrigation methods are often plagued by inefficiencies, leading to significant water wastage. This paper presents the development and validation of an autonomous drone-based irrigation system that leverages advanced image processing and machine learning techniques to optimize water usage in agriculture. The system employs standard low-cost cameras to capture high-resolution aerial images, which are processed to accurately predict the water needs of the plants and inform irrigation decisions in real-time also it can do autonomous watering by controlling the electrical water valve in the specified irrigation areas. Comprehensive field tests conducted on pepper crops demonstrate the system's ability to enhance water use efficiency and improve crop yields. By integrating state-of-the-art technologies such as TensorFlow techniques for machine lear-nig, image analysis and autonomous navigation capabilities, the proposed solution represents a significant advancement in precision agriculture. The results indicate that the autonomous drone-based irrigation system can substantially reduce water consumption while maintaining or enhancing crop productivity, thereby promoting sustainable agricultural practices.

A Study on Global Climate Mitigation and National Resource Management Based on the Green GDP Accounting Model

This article investigates the potential of Green GDP (GGDP) to replace conventional GDP as a global economic metric. By accounting for environmental degradation and natural resource depletion—factors typically excluded from traditional GDP calculations—GGDP offers a more comprehensive measure of long-term economic performance. Utilizing a linear regression model, this study examines the relationship between economic activity and climate change, with a focus on carbon emissions and water consumption. The findings reveal an inverse correlation between GGDP and rising global temperatures, suggesting GGDP’s potential to mitigate climate change by integrating the environmental costs of economic growth. A case study of China further illustrates how adopting GGDP could lead to improvements in air quality, more efficient resource management, and alignment with the country’s environmental objectives, including its commitments under the Paris Agreement. Although the transition from GDP to GGDP may face resistance, particularly in nations heavily reliant on fossil fuel consumption and resource exploitation are significant. This study underscores the necessity of incorporating environmental considerations into economic evaluations to foster a sustainable future, where economic growth can be achieved in harmony with ecological preservation. GGDP thus provides a more holistic and sustainable framework for assessing global economic performance.

Generative Artificial Intelligence and the Impact on Sustainability

An increasingly popular subcategory of Artificial Intelligence (AI) is Generative AI (GAI), which encompasses technologies capable of creating new content, such as images, text, and music, often resembling outputs made by humans. The potential impact by GAI on sustainability is multifaceted. On the positive side, generative AI can aid in optimizing processes, developing innovative solutions, and identifying patterns in large datasets related to sustainability. This can lead to more efficient resource management, reduced energy consumption, and the creation of more sustainable products. However, there are also potential negative impacts, such as increased energy consumption associated with training and running generative AI models, as well as the potential for unintended consequences or biases in the generated content. Additionally, overreliance on generative AI may lead to reduced human oversight, which could undermine holistic, interdisciplinary, and collaborative approaches to sustainability. The aim of this paper is to explore the potential impacts on sustainability by generative artificial intelligence through a review of prior research on the topic. The study was conducted with a scoping literature review approach to identify potential impacts by generative AI on sustainability. Data were collected through a search in the database Scopus during the spring semester of 2024. Keywords, relevant for the study, were combined with Boolean operators. Papers identified through the search underwent a manual screening process by the authors, in which papers were selected for inclusion or exclusion in the study based on a set of criteria. Included paper were then analyzed with thematic analysis, according to the guidelines by Braun and Clarke. A categorization matrix, based in prior research on sustainability, supported the analysis and deductive coding of collected data. Results of the study highlight generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability. These different aspects of sustainability impact make this research an important contribution for deepening the understanding of generative AI and its potential consequences for society. Findings of the study provide theoretical contribution, implications for practice, and recommendations for future research on generative AI and sustainability.

A Step Forward in Hybrid Soil Laboratory Analysis: Merging Chemometric Corrections, Protocols and Data-Driven Methods

The need to maintain soil health and produce more food worldwide has increased, and soil analysis is essential for its management. Although spectroscopy has emerged as an important tool, it is important to focus primarily on predictive modeling procedures rather than specific protocols. This article aims to contribute to a routine work sequence in a hybrid laboratory that seeks to provide the best data for its users. In this study, 18,730 soil samples from the state of Paraná, Brazil, were analyzed using three different laboratories, sensors and geometries for data acquisition. Thirty soil properties were analyzed, some using different chemical methodologies for comparison purposes. After a spectral reading, two literary protocols were applied, and the final prediction results were observed. We applied cubist models, which were the best for our population. The combination of different spectral analysis systems, with a standardized protocol using LB for the ISS detection of discrepant samples, was shown to significantly improve the accuracy of predictions for 21 of the 30 soil properties analyzed, highlighting the importance of choosing the extraction methodology and improving data quality, which have a significant impact on laboratory analyses, reaffirming spectroscopy as an essential tool for the efficient and sustainable management of soil resources.

Applying neural networks combined with Monte Carlo simulation in dam operations to obtain operational, economic, and environmental gains

The waste of potable water is a problem that affects the population's supply and the environment, raising the need for studies focusing on the adoption of efficient actions and modern technological resources, such as artificial intelligence (AI), for sustainable water management. However, in the literature there are few studies on the operational, economic and environmental benefits of using AI in dam management. Additionally, no study was found on this topic addressing the Cantareira system, located in the metropolitan region of São Paulo – Brazil, and which is one of the largest water supply systems in the world. This work presents an approach combining an Artificial Neural Network and the Monte Carlo Simulation method for floodgate control in the Cantareira system. Furthermore, we explored parameters that make the simulations of water collection and distribution more realistic. The results (RMSE=0.076 and R2=0.963) confirm the viability of using our approach to minimize water waste and flood risks, as well as to increase efficiency in water resource management. Furthermore, this study advances the state of the art by presenting a set of operational, economic, and environmental benefits directly associated with the adoption of AI in floodgate management.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Pengaruh Green Economy dan Sustainable Ecotourism terhadap Peningkatan Ekonomi Masyarakat Studi: Ekowisata Mangrove Lembung

Green economy and sustainable ecotourism are currently the main focus in efforts to advance the tourism sector which is not only economically profitable but also environmentally sustainable. The aim of this research is to determine the influence of green economy and sustainable ecotourism on improving the community's economy in the Lembung Mangrove Ecotourism. This research involved 94 respondents consisting of local communities, business actors, tourists and tourism managers in the Lembung Mangrove Ecotourism area. The method used is quantitative using the regression method to test the relationship between the variables green economy, sustainable ecotourism and economic improvement. The results of the analysis show that the two dependent variables contribute significantly to economic growth. Green economy shows a significant positive influence, indicating that the application of green economy principles such as efficient management of natural resources and environmentally friendly technology has a big impact on sustainable economic growth. Meanwhile, sustainable ecotourism also has a significant positive influence through the principles of environmental conservation and empowerment of local communities which supports increased economic prosperity. The results of this hypothesis test confirm that sustainability in the economy and ecotourism is able to create a balance between economic growth and environmental preservation. This research makes an important contribution to the development of sustainable economic policies that support the future of the ecotourism sector.

Leveraging central-surrounding receptive fields for single image dehazing

Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, Vision Transformers (ViT) have shown promising dehazing performance, leveraging their capacity for global perception through long-sequence dependencies in cost of high resource consumption. Therefore, our approach seeks to integrate the utilization of global information into the Convolutional Neural Network (CNN) framework in a more resource-efficient manner. In this paper, we introduce the Adaptive Center-Surround Receptive Field (ACSRF) network architecture inspired by the central-peripheral receptive field in biological vision for single-image haze removal. This leads to a unique receptive field mechanism that effectively combines both central and surrounding information. Our ACRSF addresses this by initially compressing global information and then merging it within the CNN, significantly boosting the capability to integrate local and global information, and effectively handling dominant color tones. Experimental results on four publicly available real-world image dehazing datasets show that our ACRSF outperforms current state-of-the-art methods in recovering global information, especially in dominant color tones. Importantly, this technology demonstrates its effectiveness in realistic scenarios, contributing significantly to improving traffic safety in adverse weather conditions. The code is available at https://github.com/JavanTang/ACSRF.

Improving the method of calculating the water balance of the Khan canal

Abstract In this article, the Morgunenkov (Khan) canal in the Chirchik-Ohangaron Basin Management for Irrigation Systems area was selected. This canal is one of the more important canal in Tashkent region because it supplies water to irrigation fields in the Urta Chirchik, Buka and Akkurgan districts of the region. In this paper the technical condition of the canal and its hydrotechnical facilities was studied. The received data was entered into the database of the processed program and the efficiency of the canal was determined. The program helps to quickly and easily identify defects that cause water wastage. This is the reason for increasing the hydraulic efficiency of the canal. In addition, the developed computer program allows for the correct distribution of water resources of the Morgunenkov (Khan) canal, taking into account the demand for water in the entire system, to prevent water shortages in the downstream areas of the canal. The benefit from the practical application of the program will be at the expense of improving the efficiency of water resources management.

Determination of the filtration consumption from the body of the soiled dam

Abstract This paper addresses the pressing issues of climate change, population growth, and the imperative for efficient water resource management. It emphasizes the importance of effectively utilizing water stored in reservoirs and accurately assessing water loss. Specifically, the study focuses on quantifying water lost through the filtration process in earthen dam bodies and examining its impact on structural integrity. Measurements were taken at the Chimkurgan reservoir when it reached the upper water level mark (Δ 480.43), assessing the filtration depression curve and the flow rate of water exiting from hydroposts at each piezometer within the dam. Statistical analysis of data collected during field research indicated an average loss of 90 liters per second through filtration from both the body and floor of the dam over a year. This article explores the relationship between filtration water consumption, the depression curve, and the geological characteristics of the dam, providing insights into effective water management strategies.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

The Importance Of Accounting Transparency In Public Management For The Strengthening Of Social Control

Background: Transparency in public accounting is essential to promote trust in institutions, ensure the efficient management of resources, and allow society to participate in social control. In Brazil, regulations such as the Fiscal Responsibility Law (LRF) and the Access to Information Law (LAI) have sought to strengthen accountability in public administration. However, challenges such as cultural resistance and low popular participation still hinder the effectiveness of these initiatives. Materials and Methods: This study is characterized as a bibliographic review, with consultation of academic publications and legal regulations available on the Google Scholar platform. Search terms such as "transparency in public accounting", "governance", "accountability" and "social control" were used. In addition, normative documents were analyzed, such as the Federal Constitution of 1988 (art. 37), the LRF and the LAI, with the objective of understanding how these norms contribute to promoting a more transparent public administration. The content analysis technique was used to organize the data into thematic categories: governance and accountability, transparency and ICTs, and social control. Results: The results indicate that, although transparency in Brazilian public administration has advanced, cultural resistance within public organizations limits the full adoption of accountability practices. Information and Communication Technologies (ICTs), such as the Managerial Treasury (TG) and SIAFI, have facilitated access to financial information, but insufficient training of public servants compromises their effectiveness. In addition, factors such as civic education and social engagement were identified as determinants for transparency to translate into effective social control. Conclusion: It is concluded that transparency and accountability are fundamental to strengthen public governance. However, a joint effort is needed involving cultural transformation, continuous training of civil servants and civic education, to encourage the active participation of society and ensure the effective use of digital tools. The integration between regulations, technology, and cultural change is essential to consolidate

Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting

This study evaluates the performance of three forecasting models—ARIMA, Prophet, and Glmnet—with the primary objective of equipping the telecommunication industry with effective tools for cellular traffic forecasting. These tools lay the foundation for efficient resource management, cost optimization, and enhanced service delivery. The study begins with dataset description and preparation, followed by the selection of traffic forecasting models, and concludes with performance evaluation based on metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The main contribution of this research is a comprehensive comparison of the three forecasting methods, aiding practitioners and researchers in identifying the best prediction model for specific contexts. The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain. Doi: 10.28991/ESJ-2024-08-06-04 Full Text: PDF