MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between  hydro-climatic variables and WQ by obtaining insights into trend changes over the years.

Agnieszka Olbert

MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years.

Civil Engineering

Research output: Contribution to conference (Published) › Poster

Original language	English (Ireland)
Publication status	Published - 1 Jan 2024
Event	MaREI Symposium 2024 at University College Cork, Ireland. 2024 - Duration: 20 Jun 2024 → …

Conference

Conference	MaREI Symposium 2024 at University College Cork, Ireland. 2024
Period	20/06/24 → …

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Apoorva Bamal, Uddin. Md Galal, Olbert Indiana

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Olbert, A. (2024). MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. . Poster session presented at MaREI Symposium 2024 at University College Cork, Ireland. 2024.

Olbert, Agnieszka. / MaREI Symposium 2024 : AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. . Poster session presented at MaREI Symposium 2024 at University College Cork, Ireland. 2024.

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title = "MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. ",

author = "Agnieszka Olbert",

year = "2024",

month = jan,

day = "1",

language = "English (Ireland)",

note = "MaREI Symposium 2024 at University College Cork, Ireland. 2024 ; Conference date: 20-06-2024",

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Olbert, A 2024, 'MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. ', MaREI Symposium 2024 at University College Cork, Ireland. 2024, 20/06/24.

MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. . / Olbert, Agnieszka.
2024. Poster session presented at MaREI Symposium 2024 at University College Cork, Ireland. 2024.

Research output: Contribution to conference (Published) › Poster

Olbert A. MaREI Symposium 2024: AI ML techniques are pivotal in climate change risk assessment, predicting meteorological events, water quality (WQ) assessment and characterizing hazard sources. Variations in hydro-climatic variables are recognized for influencing water availability and quality across various regions. Sophisticated statistical methods offer precise quantification of connections between hydro-climatic factors and WQ parameters, complementing ML AI models. Aim: To analyse the inter-relationship between hydro-climatic variables and WQ by obtaining insights into trend changes over the years. . 2024. Poster session presented at MaREI Symposium 2024 at University College Cork, Ireland. 2024.