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Inflow forecasting using regularized extreme learning machine: Haditha reservoir chosen as case study
For effective water resource management, water budgeting, and optimal release discharge from a reservoir, the accurate prediction of daily inflow is...
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Building a predictive model for hypertension related to environmental chemicals using machine learning
Hypertension is a chronic cardiovascular disease characterized by elevated blood pressure that can lead to a number of complications. There is...
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Machine Learning for Modeling Soil Organic Carbon as Affected by Land Cover Change in the Nebraska Sandhills, USA
Land cover change can affect soil organic carbon (SOC) concentrations in both top- and subsoils. Here, we propose to implement emerging machine...
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Predicting wetland soil properties using machine learning, geophysics, and soil measurement data
PurposeMachine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and...
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Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models
Landslides are a common natural disaster that can cause casualties, property safety threats and economic losses. Therefore, it is important to...
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Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning...
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Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition,...
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Predicting the Ecological Quality of Rivers: A Machine Learning Approach and a What-if Scenarios Tool
Monitoring the ecological status of rivers is essential for protecting freshwater biodiversity and ecosystem health. The main objective of this work...
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Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia
Monitoring access to safely managed drinking water services requires information on water quality. An increasing number of countries have integrated...
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Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques
Soil salinization is a widespread phenomenon leading to land degradation, particularly in regions with brackish inland aquaculture ponds. However,...
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Spatiotemporal estimation of hourly PM2.5 using AOD derived from geostationary satellite Fengyun-4A and machine learning models for Greater Bangkok
This study used four individual machine learning (ML) models (random forest, adaptive boosting, gradient boosting, and extreme gradient boosting),...
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Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning techniques in the vicinity of an open dum** yard
The groundwater around the open dum** yard is very susceptible to pollution due to infiltration of landfill leachate, which has higher...
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Determination of the surface roller length of hydraulic jumps in horizontal rectangular channels using the machine learning method
This study aims to develop and assess seven machine learning (ML) models, including an Artificial Neural Network (ANN), Gradient Boosting (GB), Extra...
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Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning
Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian...
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Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China
Predicting groundwater potential is crucial for identifying the spatial distribution of groundwater in a region. It serves as an essential guide for...
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Ensemble machine learning models for prediction of flyrock due to quarry blasting
In the mining industry, the most common approach to rock fragmentation is blasting. Blasting operations generate flyrock, which is a critical and...
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An ensemble framework-based approach for modeling stability of expansive soil slopes: fusion of machine learning algorithms and protection structure disease data
Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster...
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Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin
Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating...
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Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts
A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To...
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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore,...
