Environmental AI & DL
RNN, LSTM, BiLSTM, WaveNet, Graph Neural Networks (GNN), PINNs, Attention mechanisms, PyTorch, and TensorFlow.
About Dr. Muhammad Waqas
Environmental AI specialist with a field-to-model perspective.
Connecting agricultural engineering, hydrology, climate change, GIS, and modern deep learning for resilient food and water systems.
Research Profile
Research grounded in real environmental and climate challenges.
I bring more than 10 years of applied research and development experience across Pakistan, Thailand, and South Korea, delivering publication records, decision-support tools, and practical recommendations for water managers and agricultural policy.
Research Identity
Climate, hydrology, and agriculture with measurable impact.
This portfolio synthesizes 50 peer-reviewed publications, open-source model workflows, international collaborations, and applied monitoring systems in an integrated, transparent record.
Top 2%
Stanford / Elsevier '26
#950
World Sub-Field Rank
50
Peer-Reviewed Papers
10+
Years Applied R&D
Core Competencies
Research and technical strengths
Methodological depth spanning computational algorithms and field environmental engineering.
Climate & NWP Dynamics
Precipitation forecasting, CMIP6 climate downscaling, GCM screening, WRF cumulus parameterization, and ENSO teleconnections.
Agricultural Engineering
Irrigation optimization, crop water requirements, smart greenhouse water circulation, composting, and soil nutrient enrichment.
Geospatial & Data Science
Hydrological modeling, QGIS spatial analysis, remote sensing (GRACE, MODIS), Land Use Land Cover (LULC), and statistical modeling.
Honors & Global Recognitions
International scholarly rankings & certified benchmarks
Verified scientific recognition across global research indexes and impact databases.
Stanford University • Elsevier
Edition 2026
World's Top 2% Scientist • Rank #950
Recognized in Stanford University and Elsevier's global Top 2% Scientists list (2026 Single-Year data, TOPSCINET). Secured World Rank #950 in Environmental Engineering (Discipline: Engineering, Global Rank: 157,452, H-index: 10, Hm-index: 4) representing Gyeongsang National University, South Korea.
Scholarly Index
Certified 2026
Top1000 Scholar • World Rank #109
Certified World Rank #109 globally and Thailand Rank #2 in Hydrological Forecasting Using AI (2021–2025 impact window, Thailand Affiliation — KMUTT). Honors pioneering breakthroughs across AI precipitation forecasting and watershed hydrology.
Education
Academic qualifications and degrees
Doctorate in Environment, Climate Change, and Sustainability
King Mongkut's University of Technology Thonburi, Bangkok, Thailand
🏆 Top1000 Scholar (2026 Edition): Certified World Rank #109 • Thailand Rank #2 in AI Hydrological Forecasting (2021–2025 impact window, Thailand Affiliation).
Master of Agricultural Engineering
Bahauddin Zakariya University, Multan, Pakistan
Bachelor of Agricultural Engineering
Bahauddin Zakariya University, Multan, Pakistan