AI Climate Modeling
Machine learning and deep learning workflows for rainfall, streamflow, drought, evapotranspiration, and climate variability prediction.
Services
AI-powered climate and agriculture services for research, adaptation, and decision support.
Practical, publication-aware, and technically reproducible services for data-rich environmental, water, agrometeorology, remote sensing AI, and agritech work.
Environmental Intelligence
Cleaning, trend analysis, statistical modeling, visualization, and interpretation of weather, hydrological, agricultural, sensor, and water-quality datasets.
Research Writing and Review
Support for journal articles, literature reviews, technical reports, methodology design, publication strategy, and scientific editing.
GIS and Remote Sensing
QGIS-based mapping, land-use analysis, basin-level spatial studies, climate impact assessment, and geospatial research workflows.
Smart Agriculture Consulting
Irrigation, drainage, crop water requirements, greenhouse water systems, potential evapotranspiration, and climate-smart agriculture planning.
Training and Academic Mentoring
Hands-on training in Python, TensorFlow, AI model development, data science for agriculture, and research methods for students and teams.
Case Studies
Proof of execution across climate risk, hydrology, and smart agriculture.
Seasonal rainfall forecasting with WaveNet-LSTM
Problem: Tropical rainfall is nonlinear, seasonal, and difficult to forecast with single-method models.
Stack: Python, TensorFlow/Keras, climate-driver feature engineering, time-series validation, and benchmark comparison.
Impact: A reproducible hybrid deep learning workflow for forecast-ready rainfall intelligence and climate-risk planning.
Climate variability and crop-yield risk mapping
Problem: Agritech and research teams need spatially explicit insight into how climate variability affects crop productivity.
Stack: GIS data science, graph neural networks, climate variables, crop records, and spatial feature construction.
Impact: Decision-ready climate-crop intelligence for food-security planning, grant proposals, and field prioritization.
K-WaterGuard AI monitoring dashboard
Problem: Water-quality teams need faster ways to turn public monitoring data into alerts, maps, and daily evidence.
Stack: Python automation, public APIs, CSV history, threshold logic, spatial visualization, HTML dashboarding, and chatbot-ready backend design.
Impact: A lightweight operational system that translates live water data into usable monitoring outputs.
Best Fit