AIMC Journal:
Journal of environmental management

Showing 291 to 300 of 344 articles

Novel approach for predicting groundwater storage loss using machine learning.

Journal of environmental management
Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major grou...

Exploring the potential of utilizing unsupervised machine learning for urban drainage sensor placement under future rainfall uncertainty.

Journal of environmental management
Recently, advanced informatics and sensing techniques show promise of enabling a new generation of smart stormwater systems, where real-time sensors are deployed to detect flooding hotspots. Existing stormwater design criteria assume that historical ...

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh.

Journal of environmental management
Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a nat...

A European household waste management approach: Intelligently clean Ukraine.

Journal of environmental management
The European-wide environmental obstacles of inefficient and unsustainable recycling systems and flows constrain household waste (HW) management, endangering the circular economy. The European 2020 strategy and ongoing environmental disasters indicat...

Developing machine learning models for relative humidity prediction in air-based energy systems and environmental management applications.

Journal of environmental management
The prediction of relative humidity is a challenging task because of its nonlinear nature. The machine learning-based prediction strategies have attained significant attention in tackling a broad class of challenging nonlinear and complex problems. T...

Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production.

Journal of environmental management
The complex nature of wastewater treatment has led to search for alternative strategies such as different artificial intelligence (AI) techniques to model the various operational parameters. The present work is aimed at predicting the transmembrane p...

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates.

Journal of environmental management
Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability ...

An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration.

Journal of environmental management
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavele...

A spatially based quantile regression forest model for mapping rural land values.

Journal of environmental management
Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting e...

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP.

Journal of environmental management
This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive ...