Accurate Projections Research Articles

Taiwan’s Electricity Demand Under the COVID-19 and Supply Chain Disruption

ABSTRACT Our research aims to find the alteration in electricity consumption during supply chain disturbance following the COVID-19 pandemic, while seeking to elucidate the island-wide power blackout in Taiwan in May 2021. The inquiries addressed are as follows: initially, the income elasticity of electricity consumption decreases over time. We present evidence indicating a decoupling between electricity consumption and GDP, linked to higher overseas production ratio of export orders being produced. Secondly, as a result of the supply chain disruption, the income elasticity of electricity consumption escalates, and Taiwan experiences GDP growth. Electricity consumption increases rise through both channels. Lastly, utilizing a nonlinear model for prediction yields more accurate projections, potentially averting power outages.

GIS-BASED LANDSCAPE MANAGEMENT OF THE UZH RIVER BASIN: A STRATEGY TO ENHANCE RIVER WATER QUALITY

The article includes the findings of a study on the Uzh River basin’s surface water’s quality. This region is extremely important because a fraction of it is radioactively contaminated because of the Chornobyl nuclear power plant accident. Considering this, the goal of this work was to conduct a geo-informational analysis of the state and structure of the landscapes of the Uzh River basin, to identify the surface water quality parameters of this region, and to identify potential correlations between these parameters. For this purpose, we spatially divided the territory of the basin into separate massifs, in each of which, using the method of automatic decoding, we determined the ratio of the structural components of landscapes, their disturbance by erosion processes, and compared them with ground data on the quality of surface water of the massifs. According to the salt composition, all the investigated massifs have good quality conditions. The Uzh River and its tributaries should be categorized as 4 (slightly polluted) based on the average values of the trophosaprobological state, but the extreme values in some places shifted to 5 (moderately polluted). Most of the listed toxic substances were present at average levels and, for the most part, did not exceed the permitted concentrations for category 4-slightly polluted. Extreme concentrations frequently reached category 6 (polluted), which was primarily brought on by anthropogenic activity and the buildup of radioactive materials. It was found that the degree of erosive landscape destruction and the degree of landscape stabilization significantly influence the value of the integrated water quality index. Since the collected data, a three-dimensional model was developed, allowing for accurate evaluations and projections of changes in the quality of surface water based on the structural characteristics of the land shafts. Considering that some of these territories are currently in the zone of radioactive contamination, the planning of changes in the landscape structure should be coordinated with the potential risks of surface water quality deterioration, and the model proposed in this article will have significant applied value in addressing this issue.

Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)

Abstract. Forest dynamics need to be considered when estimating the global carbon budget. The alteration of forest structure and function under a changing climate and expanding human activity could lead to a reduction of forest canopy cover and a spread of lower-biomass ecosystems in warm and dry regions. A non-structural carbohydrate (NSC) acts as a storage buffer between carbon supplied by assimilation and carbon consumed by, inter alia, respiration, reproduction, and pests. Estimation of NSC concentrations in a tree is very important for accurate projection of future forest dynamics. We developed a new NSC module for incorporation into a spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to validate the simulated NSC dynamics with observations. NSC pools were simulated in three plant organs: leaves, trunk, and roots. The seasonal dynamics of the NSCs varied among plant species, and the sizes of the NSC pools inferred from observations differed between the boreal, temperate, and tropical climates. The NSC models were therefore validated for each of the three climatic regions at both point and global scales to assess the performance of the models. The modeled NSCs showed good agreement in seasonality with the observed NSCs at four sites – Canada (boreal), Austria and Switzerland (temperate), and Panama (tropical) – and in mean values for three climate zones derived from the global NSC dataset. The SEIB-DGVM-NSC version 1.0 is expected to enable simulation of biome shifts caused by the changes in NSC dynamics worldwide. These dynamics will contribute to changes in not only the global carbon cycle but also in forest structure and demography at a global scale.

A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era

Abstract. Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and accurate projections of future sea ice conditions. Here, we present a monthly, Arctic-basin-wide, and 25 km resolution Eulerian estimation of thermodynamic and dynamic effects on wintertime sea ice thickness from 2010–2021. Estimates of thermodynamic growth rate are determined by coupling passive microwave-retrieved snow–ice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from a weekly Alfred Wegener Institute (AWI) European Space Agency (ESA) CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) combination product (CS2SMOS), and dynamic effects are calculated as their difference. The dynamic effects are further separated into advection and residual effects using a sea ice motion dataset. Our results show new detail in these fields and, when summed to a basin-wide or regional scale, are in line with previous studies. Across the Arctic, dynamic effects are negative and about one-fourth the magnitude of thermodynamic growth. Thermodynamic growth varies from less than 0.1 m per month in the central Arctic to greater than 0.3 m per month in the seasonal ice zones. High positive dynamic effects of greater than 0.1 m per month, twice that of thermodynamic growth or more in some areas, are found north of the Canadian Arctic Archipelago, where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of less than −0.2 m per month are found where the Transpolar Drift originates, nearly equal to and opposite the thermodynamic effects in these regions. Monthly results compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019–2020. Couplets of deformation and advection effects with opposite signs are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows residual deformation effects and overall dynamic effects increasing as the winter season progresses.

COMPARATIVE ANALYSIS OF THE EFFECTIVENESSOF CORRELATION-REGRESSION AND NEURAL NETWORK MODELING IN PREDICTING ENERGY EMISSIONSOF CARBON DIOXIDE IN RUSSIA

Effective national cap-and-trade system involves accurate projections of greenhouse gas emissions for the national economy as a whole and by industry. The main source of carbon dioxide emissions in most countries of the world (including Russia) is the energy sector with 9traditional fuels (coal, gas and oil). The objective of the paper is to forecast energy emissionsof carbon dioxide in the Russian Federation by applying adequate economic and mathematical modelling methods. To achieve it, two hypotheses are consistently put forward and tested: the possibility of building a medium-term forecast of the indicator as a result of correlation and regression analysis and the one based on the formation of a Bayesian ensemble of artificial neural networks. Both hypotheses are confirmed in the empirical study. However, the second method provides a higher degree of accuracy in approximating statistical data. Therefore, within the framework of this article, the formation of medium-term forecasts of energy carbondioxide emissions in Russia is made with the help of neural network modeling. Highly accurate forecasting provides a scientific basis for effective policymakers' decisions in decarbonisation of the national economy.

Evaluating the OECD–FAO and USDA agricultural baseline projections

Abstract The Organization for Economic Cooperation and Development (OECD) in collaboration with the Food and Agricultural Organization (FAO), and the US Department of Agriculture (USDA) produce long-term agricultural projections for key indicators of agricultural commodities. These baseline projections play a key role in agricultural decision-making and policies. Our study compares the OECD–FAO and USDA baseline projections for harvested acres, production, and yield in the European Union, the US, and China for rice and wheat. We evaluate the accuracy by examining the projection error, including the magnitude of bias and variance as well as the significance of bias. We also compare the OECD–FAO and USDA projections’ predictive abilities using the modified Diebold–Mariano test. We find that the accuracy of the projections decreases as the projection horizon increases. Both agencies underestimate wheat projections in the European Union and China. USDA projections generally outperform OECD–FAO projections for most rice variables, while the reverse is found for longer-horizon EU wheat projections. This assessment may enable agencies to make more accurate projections and enable market participants to better understand these projections.

Auro-Keratoprosthesis for Visual Rehabilitation in Ocular Graft Versus Host Disease

Abstract A 36-year-old male presented with right eye graft melt post-penetrating keratoplasty (PK) for Aspergillus infection. The patient had a history of acute lymphocytic leukemia and had undergone a bone marrow transplant. He developed ocular graft versus host disease (GVHD) in both eyes and tissue adhesive application had to be performed for bilateral corneal melt. The right eye developed fungal keratitis, necessitating therapeutic keratoplasty. At presentation, the visual acuity was perception of light with an accurate projection of rays in the right eye and finger counting at 1 m in the left eye. An anterior chamber tap showed no organism on culture. Considering the non-ambulatory state of the patient and the need for early visual rehabilitation due to deteriorating mental well-being, a keratoprosthesis (KPro) surgery with amniotic membrane grafting was performed in the right eye. By post-operative day 20, the patient achieved a best-corrected visual acuity of 20/20. A PK was later performed in the other eye. It is conventionally believed that KPro is the last resort for bilateral corneal blindness and is commonly performed after multiple PK grafts have failed. However, this case highlights another indication of KPro in ocular GVHD; the rehabilitation of vision when PK will not work. In addition, this case illustrates the difference in the long-term outcomes between the two eyes, one that underwent early KPro surgery and the other that underwent early PK in ocular GVHD.

Classified Mixed Model Projections

In many practical problems, there is interest in the estimation of mixed effect projections for new data that are outside the range of the training data. Examples include predicting extreme small area means for rare populations or making treatment decisions for patients who do not fit typical risk profiles. Standard methods have long been known to struggle with such problems since the training data may not provide enough information about potential model changes for these new data values (extrapolation bias). We propose a new framework called Prediction Using Random-effect Extrapolation (PURE) which involves constructing a generalized independent variable hull (gIVH) to isolate a minority training set which is “close” to the prediction space, followed by a regrouping of the minority data according to the response variable which results in a new (but misspecified) random effect distribution. This misspecification reflects “extrapolated random effects” which prove vital to capture information that is needed for accurate model projections. Projections are then made using classified mixed model prediction (CMMP) (?) with the regrouped minority data. Comprehensive simulation studies and analysis of data from the National Longitudinal Mortality Study (NLMS) demonstrate superior predictive performance in these very challenging paradigms. An asymptotic analysis reveals why PURE results in more accurate projections. Supplementary materials for this article are available online.

Energy Demand Prediction Based on Deep Learning Techniques

The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.

Assessing climate model accuracy and future climate change in Ghana's Savannah regions

Abstract This study aimed to compare the performance of six regional climate models (RCMs) in simulating observed and projecting future climate in the Savannah zone of Ghana, in order to find suitable methods to improve the accuracy of climate models in the region. The study found that the accuracy of both individual RCMs and their ensemble mean improved with bias correction, but the performance of individual RCMs was dependent on location. The projected change in annual precipitation indicated a general decline in rainfall with variations based on the RCM and location. Projections under representative concentration pathway (RCP) 8.5 were larger than those under RCP 4.5. The changes in mean temperature recorded were 1 °C for the 2020s for both RCPs, 1–4 °C for the 2050s under both RCPs, and 1– 4 °C under RCP 4.5, and from 2 to 8 °C for the 2080s. These findings will aid farmers and governments in the West African subregion in making informed decisions and planning cost-effective climate adaptation strategies to reduce the impact of climate change on the ecosystem. The study highlights the importance of accurate climate projections to reduce vulnerability to climate change and the need to improve climate models in projecting climate in the West African subregion.

Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption

Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.

Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate

Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biases were introduced, but their benefits for impact studies and accurate streamflow projections are still debated. In this paper, we evaluated the ability of two state-of-the-art, advanced multivariate bias-adjustment methods to accurately reproduce 16 hydrological signatures, and compared their performance against two parsimonious univariate bias-adjustment methods based on a multi-criteria performance evaluation. The results indicated that all bias-adjustment methods considerably reduced biases and increased the consistency of simulated hydrological signatures. The added value of multivariate methods in maintaining dependence structures between precipitation and temperature was not systematically reflected in the resulting hydrological signatures, as they were generally outperformed by univariate methods. The benefits of multivariate methods only emerged for low-flow signatures in snowmelt-driven catchments. Based on these findings, we identified the most suitable bias-adjustment methods for water-resources management in Nordic regions under a changing climate, and provided practical guidelines for the selection of bias-adjustment methods given specific research targets and hydroclimatic regimes.

Analisis Critical Path Model yang Digunakan Dalam Manajemen Proyek dan Kontribusinya Terhadap Manajemen Mutu ISO 9001 Di Perusahaan Konstruksi Di INDONESIA

ABSTRACT
 
 Critical Path Model (CPM) has been widely used in construction project management as an effective and efficient tool for planning and managing project schedules. This study aims to analyze the contribution of CPM to quality management geothermal project at Lumut Balai, especially in the context of ISO 9001. By reviewing existing literature and case studies, this study explores how CPM can increase the effectiveness and efficiency of ISO 9001 quality management. From the analysis obtained 12 critical paths on the disciplines of Mist Eliminator, Piping, Mechanical, and Painting & Insulation. The results of this study show that the use of CPM can reduce the duration to 604 days compared to the initial schedule of 825 days. CPM can play a significant role in improving quality management in construction companies by providing clear and accurate project schedules, reducing delays, and improving overall project performance. In addition, integration of CPM with quality management tools can enhance overall quality management system and increase chance of project success. The conclusion of this study is CPM has great potential to assist construction companies in increasing the effectiveness and efficiency of ISO 9001 quality management. Construction companies in Indonesia should adopt CPM as a project management tool to ensure project success and meet ISO 9001 quality standards. This study provides insights positive about the future of CPM in construction project management and open opportunities for further research in this field.
 
 Keywords: CPM, critical path, quali

Causes and Impacts of Delays in Ethiopian Public Construction Projects (Case on Debre Markos University Construction Projects)

The construction project is regarded successful when it is given over to the owner within the time, cost, and standards necessary while minimizing delays in public construction projects. Debre Markos University (DMU) is one of the public universities undergoing massive development projects that are experiencing significant delays. This article evaluates the most prevalent delay issues encountered by Ethiopian government-funded construction projects, particularly those using DMU, analyzes their impacts, and suggests a delay management system. The study applied qualitative as well as quantitative techniques to evaluate primary and secondary data. Per the entirety statistics, the top five most important and highly ranked factors are erroneous time estimates, force majeure, lowest bid, procurement policy, and volatile markets, as well as frequent design changes. Nearly every DMU project schedule overruns up to 268.67% of its contract duration. As a result, the institution suffered from poor public relations and a high cost of supervision and contract administration (6.87% of the overall project cost). Conducting a thorough and accurate project feasibility study, awarding bids to experienced contractors, avoiding drawing discrepancies, preparing an accurate initial time and cost estimate, and strict schedule monitoring were discovered to be effective techniques for reducing delays in public construction projects.

Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state

Earth observation coupled with novel image analysis techniques now present a unique and powerful tool for the historical study of shoreline change at local to global scale. However, satellite-derived shoreline (SDS) data is limited in certain areas and is associated with large uncertainties relating to environmental factors, tidal range, and wave action. We use 14 years of monthly topographic surveys at two macrotidal sites in the UK representing end members of beach type (reflective, dissipative) to investigate the influence of tidal elevation and wave action on SDS accuracy. We find that applying appropriate water level corrections can significantly improve SDS accuracy. Results show that a different approach is required for water level definition depending on beach type and reveal that ultimately SDS accuracy is primarily controlled by beach state (beach profile shape). Accounting for tidal elevation led to substantial accuracy improvement at both sites and formed the optimal SDS strategy for the reflective site (Slapton). At the dissipative site (Perranporth) considering wave-induced water level fluctuations (wave setup and/or runup), including wave shoaling, was critical for reducing the tidally corrected SDS RMSE by a third and the mean bias by three quarters. An important realization for areas with high cloud cover such as the UK, and/or low satellite coverage, was that critically low image availability restricts temporally the type of phenomenon that can be detected (e.g., seasonal/interannual variability) and may compromise computed long-term trends. Our results suggest that the optimal approach is site-specific and depends on the shoreline translation method used and is therefore different depending on the application. We propose optimal SDS strategies to increase confidence in SDS extraction in meso-macrotidal environments with potentially low satellite useability (i.e., high cloud cover and/or low satellite coverage) depending on the spatial scale of the intended application. Long-term trends derived using this approach can reproduce trends from ground-based surveys and therefore enable more accurate projections of future shoreline position to be made.

Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2040-2060

National projections of future joint arthroplasties are useful in understanding the changing burden of surgery and related outcomes on the health system. The aim of this study is to update the literature by producing Medicare projections for revision total joint arthroplasty procedures from 2040 through 2060.The study uses 2000-2019 data from the CMS Medicare Part-B National Summary and combines procedure counts using CPT codes for revision total joint arthroplasty procedures. In 2019, revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) procedures totaled 53,217 and 30,541, respectively, forming a baseline from which we generated point forecasts between 2020 and 2060 and 95% forecast intervals (FI).On average, the model projects an annual growth rate of 1.77% for rTHAs and 4.67% for rTKAs. By 2040, rTHAs were projected to be 43,514 (95% FI = 37,429-50,589) and rTKAs were projected to be 115,147 (95% FI = 105,640-125,510). By 2060, rTHAs was projected to be 61,764 (95% FI = 49,927-76,408) and rTKAs were projected to be 286,740 (95% FI = 253,882-323,852).Based on 2019 total volume counts, the log-linear exponential model forecasts an increase in rTHA procedures of 42% by 2040 and 101% by 2060. Similarly, the estimated increase for rTKA is projected to be 149% by 2040 and 520% by 2060. An accurate projection of future revision procedure demands is important to understand future healthcare utilization and surgeon demand. This finding is only applicable to the Medicare population and demands further analysis for other population groups.

An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.

Enhancement of Indian summer monsoon rainfall by cross-equatorial dry intrusions

The Indian summer monsoon affects the lives of over 1/6 of the world’s population. Precipitation extremes during summer monsoons have dire socioeconomic impacts. Yet, the mechanisms leading to these extremes are poorly understood, making their accurate forecasts and reliable future projections a longstanding challenge. Using a Lagrangian-based method, we show that precipitation extremes link to dry air intrusions from the southern midlatitudes upper troposphere, crossing the equator, and reaching the Arabian Sea. By triggering intense ocean evaporation, these dry intrusions are associated with modulated moisture transport patterns toward India and enhanced precipitation by >17% on average, often embedding local extremes. A notable example is the excessive rain that caused the devastating Kerala flood of 2018. However, depending on the wind pattern, these dry intrusions may, in some cases, decrease rainfall over land. The emerging connection of rainfall variability with midlatitude weather systems opens opportunities for improving the forecast of precipitation extremes and understanding their future projections.

Modeling crop yield using NDVI-derived VGM metrics across different climatic regions in the USA.

An accurate, cost-efficient, and early crop yield projection is a national and global necessity. This study is aimed to achieve the national demands by proposing crop yield estimation models on the Google Earth Engine (GEE) platform. This study directly used dynamic crop phenology metrics to model soybean crop yield by considering climatic regions in the USA (e.g., Central, East, Northeast, South, Southeast, and West North Central regions). We modeled soybean yields with vegetative growth metrics (VGMs) of NDVI narrated as VGM70 (avg. NDVI of 70days from the emergence), VGM85 (avg. NDVI of 85days from the emergence), VGM98T (total NDVI of 98days from the emergence), VGM120 (avg. NDVI of 120days from the emergence), VGMmean (avg. NDVI of the growth season), VGMmax (maximum NDVI of the growth season), and climatic factors (i.e., daytime surface temperature: DST, night-time surface temperature: NST, and precipitation) from 2000 to 2019. This study further explored individual predictors and a combination of predictors in modeling crop yield for diverse climatic regions. Therefore, we proposed six crop yield linear models for each of the climatic divisions, and these models are then compared with support vector machine (SVM) models. All models showed reliable predictability based on adjusted R-square, normalized root mean square error (NRMSE), normalized mean prediction error (NMPE) parameters, and a p-value of less than 0.001.The impact of the independent predictor in the best crop yield models is discussed based on the regression weights (beta weight: β), and, the VGMmax is identified as the significant predictor in crop yield modeling for different climatic regions. Overall, this study will help the national agricultural management system for better monitoring and forecasting of soybean yield to support and manage soybean production.

Characterizing sediment functional traits and ecological consequences respond to increasing antibiotic pollution.

Current studies have shown that the taxonomic structures of ecologically important microbial communities are altered by antibiotic exposure, but the resulting effects on functional potentials and subsequent biogeochemical processes are poorly understood. However, this knowledge is indispensable for developing an accurate projection of nutrient dynamics in the future. Using metagenomic analyses, here we explored the responses of taxonomical and functional structures of a sediment microbial community, and their links with key biogeochemical processes to increasing antibiotic pollution from the pristine inlet to the outfall sites along an aquaculture discharge channel. We identified sharply contrasting sedimentary microbial communities and functional traits along increasing antibiotic pollution. Functional structures exhibited steeper distance-decay relationships than taxonomical structures along both the antibiotic distance and physicochemical distance, revealing higher functional sensitivity. Sediment enzyme activities were significantly and positively coupled with the relative abundances of their coding genes, thus the abundances of genes were indicative of functional potentials. The nitrogen cycling pathways were commonly inhibited by antibiotics, but not for the first step of nitrification, which could synergistically mitigate nitrous oxide emission. However, antibiotic pollution stimulated methanogens and inhibited methanotrophs, thereby promoting methane efflux. Furthermore, microbes could adapt to antibiotic pollution through enriched potential of sulfate uptake. Antibiotics indirectly affected taxonomic structures through alterations in network topological features, which in turn affected sediment functional structures and biogeochemical processes. Notably, only 13 antibiotics concentration-discriminatory genes contributed an overall 95.9% accuracy in diagnosing in situ antibiotic concentrations, in which just two indicators were antibiotic resistance genes. Our study comprehensively integrates sediment compositional and functional traits, biotic interactions, and enzymatic activities, thus generating a better understanding about ecological consequences of increasing antibiotics pollution. KEY POINTS: • Contrasting functional traits respond to increasing antibiotic pollution. • Antibiotics pollution stimulates CH4 efflux, while mitigating N2O emission and may drive an adaptive response of enriched sulfate uptake. • Indicator genes contribute 95.9% accuracy in diagnosing antibiotic concentrations.