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Papers
61,005 resultsShowing papers similar to Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study
ClearModeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
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.
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.
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.
Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory
Researchers applied a residual long short-term memory (LSTM) deep learning model to predict tidal levels in tropical Eastern Bintan waters, Indonesia, improving forecasting accuracy for coastal zone management. The model outperformed conventional tidal prediction methods by capturing complex nonlinear tidal dynamics in the tropical maritime environment.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse 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.
Machine Learning for Flood Resiliency—Current Status and Unexplored Directions
This review examines the current state of machine learning (ML) applications across the full flood management spectrum, finding that deep learning models like long short-term memory networks perform well for reservoir flow prediction, while reinforcement learning shows promise for managing low-impact development systems in pluvial flood control.
The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical
Researchers analyzed climate variation and human activity effects on karst spring discharge using wavelet coherence analysis, finding that anthropogenic factors including land-use changes increasingly influence groundwater dynamics alongside natural climate variability.
Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
Researchers used Google Earth Engine and Sentinel-2 satellite imagery to map seasonal surface water dynamics in Manipur, India, accurately tracking the extent and timing of water body changes across the region to support watershed planning.
Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools
This study assessed the water quality of India's River Saraswati using multiple water quality indices and forecast models, documenting declining conditions caused by diverse pollution sources and providing tools for river conservation management.
Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM)
This study introduced a hybrid modeling approach combining artificial neural networks (ANN) with hidden Markov models (HMM) for predicting microplastic pollution distribution in the environment. The ANN-HMM model outperformed single-method approaches for predicting spatial and temporal microplastic concentrations, offering an improved tool for environmental management and pollution forecasting.
An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes
Scientists applied multiple machine learning methods to predict concentrations of persistent organic pollutants in the Great Lakes, finding that LSTM neural networks outperformed simpler models for these complex time-series patterns. Similar predictive modeling could track microplastic concentrations in large water bodies over time.
Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine
Researchers developed a hybrid machine learning framework to predict landfill leachate and groundwater quality, providing a robust monitoring tool to assess contamination risk to water resources near landfill sites.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.
Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers
Researchers developed a water cycle algorithm-optimized neural network to predict electrical power output from combined cycle power plants, demonstrating improved prediction accuracy compared to standard optimization algorithms on a publicly available dataset.
Machine Learning for Flood Resiliency—Current Status and Unexplored Directions
This systems review assessed machine learning applications across the flood management spectrum—reservoir inflow prediction (LSTM), levee failure detection (CNN), and low-impact development control (reinforcement learning). The paper is focused on flood resilience engineering and does not contain microplastics research.
Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen
Researchers developed and compared neural network models for predicting dissolved oxygen concentrations in water using machine learning metaheuristic algorithms. Dissolved oxygen is a key indicator of aquatic ecosystem health, and accurate prediction tools support monitoring of water bodies affected by plastic and other pollutants.
Water environment response of urban water networks in the Pearl River Delta (China) under the influence of typhoon rain events
This study used artificial neural networks to model water quality parameters in the urban water network of China's Pearl River Delta region, examining how typhoon rain events affect pollutant concentrations. The research contributes to understanding how extreme weather events — which are increasing with climate change — flush pollutants including microplastics from urban environments into waterways.
Data-Driven Models’ Integration for Evaluating Coastal Eutrophication: A Case Study for Cyprus
Researchers developed and compared two artificial neural network models trained on in situ monitoring data to predict coastal eutrophication in Cypriot waters, demonstrating a data-driven approach to environmental monitoring that supports the aquaculture industry's regulatory compliance requirements.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
A Hybrid Deep Learning Model for Wind and Solar Power Forecasting in Smart Grids
Researchers developed a hybrid deep learning model combining multiple neural network architectures to improve wind and solar power forecasting in smart grids, addressing limitations of traditional models in handling the complex, non-linear, and time-varying nature of renewable energy output.
Identification and Prediction of Crop Waterlogging Risk Areas under the Impact of Climate Change
Researchers developed a crop waterlogging risk identification model to predict areas vulnerable to agricultural flooding under climate change scenarios, aiming to support disaster prevention planning in affected farming regions.