Search
Search Results
-
Data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios
Effective relief reduces damages and protects people during natural disasters, such as earthquakes. This research proposes a data-driven model based...
-
Combined advanced oxidation dye-wastewater treatment plant: design and development with data-driven predictive performance modeling
The recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental...
-
Divergent data-driven estimates of global soil respiration
The release of carbon dioxide from the soil to the atmosphere, known as soil respiration, is the second largest terrestrial carbon flux after...
-
Do fossil fuel firms reframe online climate and sustainability communication? A data-driven analysis
Identifying drivers of climate misinformation on social media is crucial to climate action. Misinformation comes in various forms; however, subtler...
-
Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin
Physically based or data-driven models can be used for understanding basinwide hydrological processes and creating predictions for future conditions....
-
Data-Driven Modeling for the Prediction of Stack Gas Concentration in a Coal-Fired Power Plant in Türkiye
AbstractIn this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired...
-
A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability
Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management...
-
Data-Driven Circular Economy of Biowaste to Bioenergy with Conventional Prediction Modelling and Machine Learning
Rapid population growth has not only increased energy demand, but waste generation that has increased and introduced emerging pollutants into waste...
-
Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction
Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease...
-
Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model
At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by...
-
Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran
Streamflow forecasting is a critical aspect of water resource management, particularly in regions where surface water is scarce. In this study, we...
-
Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective
The low-carbon transformation of manufacturing enterprises is considered to be imperative to achieve carbon neutrality. Therefore, we propose a...
-
Data-driven sustainability evaluation and manufacturing system enhancement from economic, environmental, social, and sustainability perspectives
Sustainable manufacturing is crucial to achieving carbon neutrality targets. However, research on the sustainability of manufacturing systems is...
-
Comparison of optimized data-driven models for landslide susceptibility map**
Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have...
-
Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms
In recent decades, the simulation and modeling of water quality parameters have been useful for monitoring and assessment of the quality of water...
-
Simulating the climate driven runoff in data-scarce mountains by machine learning and downscaling reanalysis data
Lacking climatic data because of scarce meteorological stations make it difficult to assess the non-stationary climatic-runoff processes driven by...
-
A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters
Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on...
-
A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset
Recently, several papers have been published regarding the use of preprocessing models, such as Discrete Wavelet, in Data-Driven Forecasting...
-
VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM
Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in...
-
Data-driven predictive modeling of PM2.5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India
The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM 2.5 concentration at a location in Delhi. For this...
