Author comment: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R0/PR1 DOI Creative Commons
Babak Zolghadr‐Asli

Published: Dec. 23, 2023

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Smart Water Management and Resource Conservation DOI
Rajeev Kumar, Arti Saxena

Advances in electronic government, digital divide, and regional development book series, Journal Year: 2024, Volume and Issue: unknown, P. 235 - 262

Published: Nov. 15, 2024

Water is essential to every living being. management and resource conservation very important provide safe clean water all. Resources of have been polluted contaminated due increasing population urbanization. Irrigation hydropower reservoir are other sources responsible for stress on earth. The main aim smart cities urban development everyone at low cost in sustainable ways. Thus, it necessary conserve resources manage the smartly. Use non-conventional irrigation, aquaculture aquifer recharge one solutions decrease use fresh these purposes. Machine learning solution managing conserving resources. Various machine models applied prediction tasks. However, deep categorization regression task. chapter objective cities.

Language: Английский

Citations

1
Decision: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R1/PR6 DOI Creative Commons
Babak Zolghadr‐Asli, Ahmad Ferdowsi, Dragan Savić

et al.

Published: March 5, 2024

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Citations

0
Recommendation: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R0/PR2 DOI Creative Commons
Babak Zolghadr‐Asli, Ahmad Ferdowsi, Dragan Savić

et al.

Published: Feb. 7, 2024

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Citations

0
Decision: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R0/PR3 DOI Creative Commons
Babak Zolghadr‐Asli, Ahmad Ferdowsi, Dragan Savić

et al.

Published: Feb. 8, 2024

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Citations

0
Recommendation: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R1/PR5 DOI Creative Commons
Babak Zolghadr‐Asli, Ahmad Ferdowsi, Dragan Savić

et al.

Published: March 5, 2024

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Citations

0
Author comment: A call for a fundamental shift from model-centric to data-centric approaches in hydroinformatics — R1/PR4 DOI Creative Commons
Babak Zolghadr‐Asli

Published: Feb. 20, 2024

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still progress grand scheme things, yet for most part, such attempts been centered around themselves, their internal computational architecture, that is, model-centric approach. These endeavors can certainly pave way more tailor-fitted capable producing accurate results. However, a perspective often neglects fundamental assumption models, which importance reliability, correctness, accessibility data used constructing them. This challenge arises from prevalent paradigm thinking field. An alternative approach, however, would prioritize placing at focal point, focusing on systematically enhancing current datasets devising to improve collection schemes. suggests shift toward data-centric Practically, this not without challenges necessitates smarter rather than an excessive one. Equally important ethical data, making it available everyone while safeguarding rights individuals other legal entities involved process.

Language: Английский

Citations

0
Groundwater Model for Karst and Pelitic Aquifer Systems from a Semi-Arid Region Under Climate Change Scenarios: A Case Study in the Vieira River Watershed, Brazil DOI Open Access

Apolo Pedrosa Bhering,

I.M.H.R. Antunes,

Gustavo Nascimento Catão

et al.

Water, Journal Year: 2024, Volume and Issue: 16(21), P. 3140 - 3140

Published: Nov. 2, 2024

Water scarcity is a global issue, especially in semi-arid and arid regions where precipitation irregularly distributed over time space. Predicting groundwater flow heterogeneous karst terrains, which are essential water sources, presents significant challenge. This article integrates geology, hydrology, monitoring to develop pioneering conceptual numerical model of the Montes Claros Region (Vieira River Watershed, Brazil). was evaluated under various climate change scenarios, considering changes rainfall, consumption, population growth current century. The results indicate that decline table levels inevitable, primarily driven by high pumping rates rather than rainfall fluctuations. underscores urgent need for improved monitoring, upgrading, more importantly, targeted resource management Claros.

Language: Английский

Citations

0
Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach DOI

Yuxin Du,

Jing Fan, Ari Happonen

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 197 - 216

Published: Jan. 1, 2024

Language: Английский

Citations

0
Editorial: Navigating uncharted waters: the risks of machine learning in the hands of non-experts DOI
Stephen Nash

Proceedings of the Institution of Civil Engineers - Water Management, Journal Year: 2024, Volume and Issue: 177(6), P. 359 - 360

Published: Nov. 15, 2024

Language: Английский

Citations

0
Large-scale groundwater pollution risk assessment research based on artificial intelligence technology: A case study of Shenyang City in Northeast China DOI Creative Commons
Lingjun Meng,

Yuru Yan,

Haihua Jing

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112915 - 112915

Published: Dec. 1, 2024

Language: Английский

Citations

0