Advanced Modelling of Soil Organic Carbon Content in Coal Mining Areas Using Integrated Spectral Analysis: A Dengcao Coal Mine Case Study

International Journal of Innovative Science and Research Technology (IJISRT), Journal Year: 2024, Volume and Issue: unknown, P. 2844 - 2853

Published: June 14, 2024

Effective modelling and integrated spectral analysis approaches can advance precision. To develop an forecast of soil organic carbon (SOC), this research investigated a mining coal in Dengcao Coal Mine Area, Zhengzhou. The study utilizes the Lasso Ranger algorithms were utilized band analysis. Four primary models employed during process include Artificial Neural Network (ANN), Support Vector Machine, Random Forest (RF), Partial Least Squares Regression (PLSR). ideal model was chosen. results showed that, contrast to when collection based on algorithm modelling, precision higher it algorithm. ANN had goodness acceptance, developed by RF steadiest consequences. Based results, distinct method is proposed for assortment at earlier stage SOC. be used check particles, or chosen prediction different statistics sets, which appropriate create SOC content Area. This avails position Analysis Advanced Modelling Soil Organic Carbon Content Sources alongside theoretical foundation innovating portable device assessment habitats. might significant changing monitoring environmental areas.

Language: Английский

---

Geological survey techniques and carbon storage: Optimizing renewable energy site selection and carbon sequestration

Open Access Research Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 11(1), P. 039 - 051

Published: May 5, 2024

Geological survey techniques play a crucial role in optimizing site selection for renewable energy projects and identifying suitable locations carbon storage to mitigate climate change. This abstract provides an overview of how geological can be used achieve these objectives. Renewable development, particularly solar wind power, requires careful maximize generation efficiency minimize environmental impacts. surveys are instrumental assessing factors such as subsurface geology, topography, soil composition, hydrological conditions. These help identify with optimal or resources geologic conditions infrastructure development. Additionally, essential sites storage, critical component capture (CCS) technologies aimed at reducing greenhouse gas emissions. formations, deep saline aquifers, depleted oil reservoirs, unmineable coal seams, serve reservoirs captured dioxide (CO2). characterize formations assess their suitability long-term CO2 considering porosity, permeability, sealing integrity. Optimizing comprehensive understanding geology Advanced techniques, seismic imaging, remote sensing, geophysical surveys, acquiring detailed data. enable scientists engineers suitability, evaluate risks, design effective mitigation measures. In conclusion, invaluable tools storage. By leveraging stakeholders make informed decisions that promote sustainable development the impacts

Language: Английский

---

Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions

Hygiene and Environmental Health Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100114 - 100114

Published: Oct. 1, 2024

Language: Английский

---

Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2664 - 2664

Published: March 24, 2024

Agricultural technology integration has become a key strategy for attaining agricultural sustainability. This study examined the of in practices towards sustainability, using Greece as case study. Data were collected questionnaire from 240 farmers and agriculturalists Greece. The results showed significant positive effect on with p-values indicating strong statistical relevance (types used: p = 0.003; factors influencing adoption: 0.001; benefits integration: 0.021). These highlight effects that cutting-edge like artificial intelligence, Internet Things (IoT), precision agriculture have improving resource efficiency, lowering environmental effects, raising yields. Our findings cast doubt conventional dependence intensive, resource-depleting farming techniques point to move toward more technologically advanced, sustainable approaches. research advances conversation by showcasing how well may improve sustainability Greek agriculture. emphasizes significance infrastructure investment, supporting legislation, farmer education order facilitate adoption technology.

Language: Английский

---

Recent Advances in Dielectric Properties-Based Soil Water Content Measurements

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(8), P. 1328 - 1328

Published: April 10, 2024

Dielectric properties are crucial in understanding the behavior of water within soil, particularly soil content (SWC), as they measure a material’s ability to store an electric charge and influenced by other minerals soil. However, comprehensive review paper is needed that synthesizes latest developments this field, identifies key challenges limitations, outlines future research directions. In addition, various factors, such salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, environmental influence measurement dielectric permittivity Therefore, aims address gap critically analyzing current state-of-the-art properties-based methods for SWC measurements. The motivation increasing importance precise data applications agriculture, monitoring, hydrological studies. We examine time domain reflectometry (TDR), frequency (FDR), ground-penetrating radar (GPR), remote sensing (RS), capacitance, which accurate cost-effective, enabling real-time resource management health through measuring travel electromagnetic waves reflection coefficient these waves. can be estimated using approaches, TDR, FDR, GPR, microwave-based techniques. These made possible loss factor with SWC. available further synthesized on basis mathematical models relating apparent providing updated their development, applications, monitoring. It also analyzes recent calibration models, algorithms, challenges, trends estimating By consolidating advances highlighting remaining article guide researchers practitioners toward more effective strategies

Language: Английский

---

Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 6, 2025

Improper management of soil resources leads to the destruction organic carbon (SOC) stock and, as a result, reduction quality, well accelerating process climate change through release SOC into atmosphere. This study was conducted evaluate potential different simulation models map spatial variability affected by land use in area Qarasu watershed Kermanshah province, west Iran. Map sampling points prepared using Latin hypercube method. A total 168 observation were selected and profile dug described these points. The samples taken horizon determine content laboratory. mapped kriging geostatistical method area. changes simulated multivariate analysis machine learning methods including generalized linear model (GLM), additive (LAM), cubist, random forest (RF), support vector (SVM) models. Comprehensive measurement data is utilized develop validate predictive Predictor variables included 16 topographic two vegetation, six parent material, four climatic variables. In-depth statistical analyses are proposed performance. results showed that ranged from 0.19 8.44 percent uses. spherical variogram with MAE = 0.41 best fits interpolate ordinary LAM estimated wider range (SOC 0.18–4.82%) among model. However, RF (R2 0.64 RMSE 0.58%) most accurate predicting quantity comparing other It can be used reliable predict similar semiarid regions West Asia Among predictor variables, material's intrinsic properties topography had greatest effect variability.

Language: Английский

---

A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies

Journal of Soils and Sediments, Journal Year: 2024, Volume and Issue: 24(11), P. 3556 - 3571

Published: Oct. 5, 2024

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of estimates SOC using remote sensing (RS) machine learning (ML) techniques. It showcases the historical growth thematic evolution in research, aiming to amplify understanding estimation themes provide scientific support change adaptation mitigation. Materials Methods Employing extensive literature database analysis, network clustering techniques, reviews 1,761 articles on RS technologies 490 employing both ML technologies. Results Discussion The results indicate that satellite-based RS, particularly Landsat series, predominant other associated studies, with North America, China, Europe leading evaluations Africa having low adopting technology. Trends research demonstrate an from basic mapping advanced topics such as (C) sequestration, complex modeling, big data utilization. Thematic clusters co-occurrence suggest interplay between technology development, environmental surveys, properties, dynamics. Conclusion highlights synergy ML, techniques proving be critical accurate estimation. These findings are crucial estimation, informed strategic decision-making.

Language: Английский

---

Monitoring and Modeling the Soil‐Plant System Toward Understanding Soil Health

Reviews of Geophysics, Journal Year: 2025, Volume and Issue: 63(1)

Published: Jan. 25, 2025

Abstract The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of biota and biotic processes that impact properties. Consequently, shifted a predominantly physicochemical approach incorporating ecological, biological molecular microbiology indicators. This shift enables comprehensive exploration microbial community properties their responses environmental changes arising climate change anthropogenic disturbances. Despite the increasing availability indicators (physical, chemical, biological) data, holistic mechanistic linkage not yet been fully established between functions across multiple spatiotemporal scales. article reviews state‐of‐the‐art monitoring, understanding how soil‐microbiome‐plant contribute feedback mechanisms causes in properties, as well these have functions. Furthermore, we survey opportunities afforded by soil‐plant digital twin approach, integrative framework amalgamates process‐based models, Earth Observation data assimilation, physics‐informed machine learning, achieve nuanced comprehension health. review delineates prospective trajectory for monitoring embracing systematically observe model system. We further identify gaps opportunities, provide perspectives future research enhanced intricate interplay hydrological processes, hydraulics, microbiome, landscape genomics.

Language: Английский

---

From soil health to agricultural productivity: The critical role of soil constraint management

CATENA, Journal Year: 2025, Volume and Issue: 250, P. 108776 - 108776

Published: Jan. 30, 2025

Language: Английский

---

Exploring the Role of Nature-based Solutions and Emerging Technologies in Advancing Circular and Sustainable Agriculture: An Opinionated Review for Environmental Resilience

Cleaner and Circular Bioeconomy, Journal Year: 2025, Volume and Issue: unknown, P. 100142 - 100142

Published: Feb. 1, 2025

Language: Английский

---