Machine learning modeling of lake chlorophyll content in a data scarce region (Northern Patagonia, Chile): insights for environmental monitoring DOI
Luciano Caputo, Cristian Ríos Molina,

Roxanna Ayllon

et al.

Inland Waters, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: May 28, 2024

Among South America, Chile is highly susceptible to climate change impacts on water resources and ecosystems. Chilean lakes rivers have been impacted by anthropogenic activities leading chemical pollution eutrophication. Concerns for conservation management of has led the current development secondary norms environmental quality Northern Patagonian lakes. In this context, we analyze historical limnological databases (1979-2022) these utilizing Random Forest (RF) models. After filtering, retained data 11 including key variables of: dissolved oxygen, electric conductivity, transparency, temperature, pH, total nitrogen, phosphorus chlorophyll-a. This dataset yielded robust results, accurately predicting chlorophyll-a content. Furthermore, added lake geomorphological parameters, enhancing performance model. Our study demonstrates need improve long-term monitoring programs, optimizing recording decreasing costs. We conclude that studied generally maintain their oligotrophic characteristics, however further analysis suggests are more sensitive nitrogen loading than phosphorus. results highlight implement adaptative plans at watershed level regulate contamination (from agriculture, pisciculture urbanization). The features selected RF, coupled with assessment trophic state variation, allow establishment permissible concentration thresholds major nutrients other sentinel informing regulations such as quality. Lastly, enhanced RF modeling when geographical parameters unveils standardize integrate in practices.

Language: Английский

A Critical Insight and Evaluation of AI Models for Predictive Maintenance under Industry 4.0 DOI

Tasneem Kagzi,

Kamlendu Pandey

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS), Journal Year: 2024, Volume and Issue: 7, P. 1 - 15

Published: Feb. 24, 2024

An efficient production line and regular preventive maintenance is a key of success for any manufacturing industry as it avoids the costly breakdowns principal factor increase in revenue net profits. One major constraints failing to understand cycle internal moving parts like bearings which are machinery. The such requires comprehensive technical understanding experience may not be available all type units. Recent developments AI techniques - Machine learning, Deep learning Random Forests can help us predict by taking factors consideration. We hereby present detailed survey work done predictive modeling bearing failure. This paper provides systematic literature review state art proposed various researchers Predictive Maintenance determination Remaining Useful Life (RUL) component. presents information certain Industrial functions failures respected maintenance, models based on Ensemble Learning algorithms, Neural Network algorithms some other miscellaneous also types sensors used condition monitoring, datasets etc.

Language: Английский

Citations

1
Quantifying the relative importance of agricultural land use as a predictor of catchment nitrogen and phosphorus concentrations DOI Creative Commons

Merry Crowson,

Nathalie Pettorelli, Nick J. B. Isaac

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 954, P. 176589 - 176589

Published: Sept. 30, 2024

Language: Английский

Citations

1
A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning DOI
Jamie Coble, Matthew Alberts,

Sam St. John

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Language: Английский

Citations

1
A novel approach to identify priority areas for optimal nutrient management in mixed land-use watersheds through nutrient budget assessment DOI
Deok-Woo Kim, Eu Gene Chung, Eun Hye Na

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 357, P. 120645 - 120645

Published: April 1, 2024

Language: Английский

Citations

1
Machine learning modeling of lake chlorophyll content in a data scarce region (Northern Patagonia, Chile): insights for environmental monitoring DOI
Luciano Caputo, Cristian Ríos Molina,

Roxanna Ayllon

et al.

Inland Waters, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: May 28, 2024

Among South America, Chile is highly susceptible to climate change impacts on water resources and ecosystems. Chilean lakes rivers have been impacted by anthropogenic activities leading chemical pollution eutrophication. Concerns for conservation management of has led the current development secondary norms environmental quality Northern Patagonian lakes. In this context, we analyze historical limnological databases (1979-2022) these utilizing Random Forest (RF) models. After filtering, retained data 11 including key variables of: dissolved oxygen, electric conductivity, transparency, temperature, pH, total nitrogen, phosphorus chlorophyll-a. This dataset yielded robust results, accurately predicting chlorophyll-a content. Furthermore, added lake geomorphological parameters, enhancing performance model. Our study demonstrates need improve long-term monitoring programs, optimizing recording decreasing costs. We conclude that studied generally maintain their oligotrophic characteristics, however further analysis suggests are more sensitive nitrogen loading than phosphorus. results highlight implement adaptative plans at watershed level regulate contamination (from agriculture, pisciculture urbanization). The features selected RF, coupled with assessment trophic state variation, allow establishment permissible concentration thresholds major nutrients other sentinel informing regulations such as quality. Lastly, enhanced RF modeling when geographical parameters unveils standardize integrate in practices.

Language: Английский

Citations

1