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Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners
Streamflow forecasting holds a pivotal role in the effective management of water resources, flood control, hydropower generation, agricultural...
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High performance machine learning approach for reference evapotranspiration estimation
Accurate reference evapotranspiration (ET 0 ) estimation has an effective role in reducing water losses and raising the efficiency of irrigation water...
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Haze prediction method based on stacking learning
In recent years, with the rapid economic development of our country, environmental problems have become increasingly prominent, especially air...
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Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility map** over the North-Moungo perimeter, Cameroon
BackgroundThe Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion...
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A comparative study of heterogeneous and homogeneous ensemble approaches for landslide susceptibility assessment in the Djebahia region, Algeria
This study aims to compare the performance of ensembles according to their inherent diversity in the context of landslide susceptibility assessment....
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Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage
Machine learning was used to provide data for further evaluation of potential extraction of octathiocane (S 8 ), a commercially useful by-product, from...
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Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods
Precise and efficient landslide displacement prediction is crucial for improving the effectiveness of landslide warning systems. Numerous time series...
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A new implementation of stacked generalisation approach for modelling arsenic concentration in multiple water sources
AbstractThe current study proposes an effective machine learning model based on a stacked generalisation technique for predicting arsenic content in...
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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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Robust stacking-based ensemble learning model for forest fire detection
Forests reduce soil erosion and prevent drought, wind, and other natural disasters. Forest fires, which threaten millions of hectares of forest area...
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Map** groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh
A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, map** groundwater potentiality (GWP) is necessary for...
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A perceptible stacking ensemble model for air temperature prediction in a tropical climate zone
Bangladesh is one of the world’s most susceptible countries to climate change. Global warming has significantly increased surface temperatures...
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Forecasting China’s carbon emission intensity and total carbon emissions based on the WOA-Stacking integrated model
China ranks first globally in carbon emissions. Accurate carbon emissions forecasting is crucial for devising effective mitigation strategies and has...
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Teaching, learning and assessment methods for sustainability education on the land–sea interface
The Land–Sea Interface (LSI) is where land and sea meet, not only in physical terms, but also with regards to a large variety of ecological and...
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Real-time error correction for flood forecasting based on machine learning ensemble method and its uncertainty assessment
Real-time error correction is an effective measure to improve forecast accuracy. This paper develops a real-time error correction model based on...
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Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy
Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the...
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Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone
The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is...
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Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area
Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking . In this study, stacking of five base...
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Prediction of Groundwater Arsenic Risk in the Alluvial Plain of the Lower Yellow River by Ensemble Learning, North China
Northern Henan is an important grain base in Henan Province. ArsenicArsenic enrichmentEnrichment in groundwaterGroundwater used for agricultural...
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Blast Furnace Thermal State Prediction Based on Multiobjective Evolutionary Ensemble Neural Networks
AbstractBlast furnace ironmaking is the largest energy-consuming and greenhouse gas-emitting process in the iron and steel industry. As a key...
