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Papers
61,005 resultsShowing papers similar to Machine Learning-Based Models for Basic Sediment & Water and Sand-Cut Prediction in Matured Niger Delta Fields
ClearAn Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers
A data-driven machine learning model was developed to predict total sediment load in rivers using readily available hydrological and morphological variables, outperforming conventional empirical sediment transport equations in accuracy. The model provides a practical tool for river management applications where comprehensive physical measurements are unavailable.
Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network
This study evaluated and predicted production yield in a plastic manufacturing company using artificial neural network modeling. Predictive tools that improve manufacturing efficiency can reduce material waste and off-specification plastic products that may contribute to environmental plastic pollution.
Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers
Researchers developed a neural network-based model to predict the impact energy of hydraulic breakers using 1,451 data points covering parameters such as working pressure, flow rate, chisel diameter, nitrogen gas pressure, operating frequency, and power. Comparative analysis with linear regression and correlation methods confirmed the neural network approach provided the most reliable predictions across breaker classes.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic pollution.
Evaluation of ANFIS Predictive Ability Using Computed Sediment from Gullies and Dam
This study developed and validated an adaptive neuro-fuzzy model to predict sediment deposition in a dam using rainfall, slope, particle size, and velocity as inputs. Accurate sediment prediction models support reservoir management and can be adapted to track how microplastic particles move and deposit in aquatic systems.
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network
This paper is not relevant to microplastics research — it focuses on optimizing electrocoagulation treatment of oil industry wastewater and developing empirical formulas for chemical oxygen demand removal.
Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation
Researchers used an artificial neural network model to predict heavy metal and phthalate concentrations in soil based on land use and soil type, achieving high predictive accuracy (r² values of 0.81–0.97), offering a practical tool for environmental risk screening without exhaustive chemical sampling.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.
Machine Learning to Predict the Adsorption Capacity for Microplastics
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
Predicting Aquaculture Water Quality Using Machine Learning Approaches
Researchers compared four machine learning approaches for predicting water quality parameters in industrial aquaculture systems, finding that back propagation and radial basis function neural networks outperformed support vector machine models for most parameters. The models achieved sufficient accuracy to support real-time management decisions without continuous in-situ monitoring.
Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models
Researchers used satellite imagery and machine learning to predict where microplastics accumulate on sandy beaches along Brazil's northern coast. They found that beach shape, slope, and proximity to urban areas were strong predictors of microplastic deposits. The study demonstrates that geotechnology tools can help identify pollution hotspots without costly field sampling at every location.
Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
Researchers used artificial neural network models to predict heavy metal contamination in soils near illegal landfills close to agricultural areas. The study found that illegal landfilling significantly impacts surrounding soil quality and proposes these predictive models as effective tools for environmental risk management and decision-making.
Analysing of rainfall-runoff modelling using a hybrid DNN-SGD optimisation in Sub Basin of Brahmaputra River, India
Researchers developed a hybrid deep neural network optimized with stochastic gradient descent (DNN-SGD) for rainfall-runoff modelling in a sub-basin of the Brahmaputra River in India, evaluating model accuracy using coefficient of determination, mean squared error, and root mean squared error metrics. The study demonstrated that the DNN-SGD model achieved superior runoff prediction performance compared to conventional hydrological approaches in the monsoon-dominated river basin.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Machine learning approach for automated beach waste prediction and management system: A case study of Mumbai
Researchers developed a machine learning system to predict beach waste generation patterns in Mumbai, aiming to enable more effective and automated waste management for one of the world's most polluted coastal cities.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Coupling life prediction of bending very high cycle fatigue of completion strings made of different materials using deep wise separable convolution
Not relevant to microplastics — this study uses deep learning to predict the fatigue life of nickel-based alloy completion strings used in oil well engineering, with no connection to microplastic pollution.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.
Coupled intelligent prediction model for medium- to long-term runoff based on teleconnection factors selection and spatial-temporal analysis
This paper developed a coupled intelligent prediction model for medium- to long-term river runoff forecasting, combining teleconnection analysis with machine learning to improve prediction accuracy for water resource planning. The model outperformed traditional hydrological forecasting methods.
Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study
Researchers developed hybrid SARIMA and artificial neural network models to predict reservoir water levels at Red Hills Reservoir in India, aiming to improve water resource management under changing climatic conditions.
Machine Learning Predictive Model for Permeability Alteration Induced by Microplastics Migration in Porous Media
Researchers developed a machine learning model (MLM) trained on a dataset of over 190,000 data points generated via combined Computational Fluid Dynamics and Discrete Element Method simulations to predict how microplastic particles clog pore throats and impair permeability in porous media. The three-component MLM achieved 95% accuracy in predicting clogged throats and an R-squared value of 0.99 for permeability impairment prediction, offering a new tool for assessing microplastic impacts on groundwater systems.
Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks
Machine learning models accurately predicted how much heavy metals like cadmium, lead, and zinc would adsorb onto microplastics in rivers, lakes, and oceans, based on factors like metal concentration and water salinity. Aged microplastics showed higher metal sorption capacity than virgin plastics, and predicted values matched real-world field measurements.
Geochemical Background and Correlation Study of Ground Water Quality in Ebocha-Obrikom of Rivers State, Nigeria
Researchers assessed groundwater quality in the Ebocha-Obrikom area of Nigeria's Niger Delta, a region heavily affected by oil industry pollution. They measured physicochemical properties and heavy metal concentrations across multiple well water samples over a year-long period. The findings revealed correlations between certain heavy metals and water quality parameters, providing baseline data for monitoring contamination in the region.