Remote sensing
Satellite and airborne observation of water bodies, surface properties, and change over time.
Research • Data • AI • Water • Climate
Farzad Asgari
Water & Hydraulic Engineer
Research Associate
Climate / AI Researcher
Programming Lecturer
Bridging water science, computational modelling, remote sensing, and artificial intelligence.
● model active ∂/∂t λ · f(x)
About / 00
Researcher. Engineer. Educator.
Water and hydraulic engineering
My background is in the physics of water, how it moves, what it carries, and how those processes can be measured and modelled at the scale of real systems.
Computational and data-driven research
I work where measurement meets computation: remote sensing, signal processing, and machine learning applied to environmental and hydraulic problems, with hybrid models that keep physical reasoning in the loop.
Scientific software
Research is only reproducible if the code is. I build and release open scientific tooling so that methods can be inspected, reused, and improved by others.
Teaching
I teach programming, machine learning, and applied AI, mostly to engineers and scientists who want computation as a working instrument, not a black box.
status: research_associate · domains: 5 · mode: active
Water & Hydraulics
Remote Sensing
Machine Learning
Data Science
Signal Processing
Scientific Computing
Research / 01
What I work on
Eight threads that keep converging on the same question: how do you turn a noisy physical measurement into something a model can reason about?
Signal processing
Denoising, despiking, and reconstructing instrument records before they reach a model.
Environmental modelling
Climate extremes, water quality, and evaporation as coupled environmental systems.
Hydraulics
Flow behaviour, turbulence measurement, and the instrumentation that captures both.
Data-driven modelling
Letting observations define the model structure where governing equations fall short.
Machine learning
Regression, classification, and ensemble methods for environmental prediction tasks.
Deep learning
Sequence and convolutional architectures for spatial and temporal environmental data.
Hybrid modelling
Physics and learning in one pipeline, so predictions stay physically defensible.
Method / 02
From physical systems to intelligent models
Every project I take on moves along the same path. The interesting work is almost always in the middle, where a measurement stops being noise and starts being evidence.
Physical world
rivers · lakes · flows
Sensors
satellites · adv · gauges
Data
raw · irregular · noisy
Signal processing
filter · despike · fill
Machine learning
train · tune · validate
Prediction
forecast · estimate
Scientific insight
decision · publication
Projects / 03
Research and software
Selected work across remote sensing, climate extremes, and scientific tooling.
Live model
Featured · Remote sensing + deep learning
Water quality & lake evaporation
- Research question
- Can satellite observation and deep learning estimate water quality indicators and lake evaporation reliably enough to replace sparse in-situ measurement?
- Method
- Remote sensing retrieval combined with deep learning models for evaporation estimation, validated against ground observations.
- Technologies
- Python TensorFlow Google Earth Engine Remote Sensing Hybrid Modelling
Climate extremes
Compound drought & heat events
Statistical modelling of concurrent drought and heat extremes, and how those compound events propagate into electricity demand.
Statistics
Python
Climate data
Climate Extreme Events
Paralellize Programming
Electricity
Scientific software
Publications / 04
Published work
-
ProADV: A toolkit for enhancing water dynamics research using acoustic doppler velocimeter devices
SoftwareX
-
Unleashing the power of three-dimensional kernel density estimation for Doppler Velocimeter data despiking
Measurement
-
Exploring the role of signal pollution rate on the performance of despiking velocity time-series algorithms
Flow Measurement and Instrumentation
-
A New Software Package and Filtering Algorithm Development for Despiking Doppler Velocimeter Data
Proceedings of the 39th IAHR World Congress
0
Published research papers
0
Research & software projects
0
Technical domains
Multiple
Teaching & workshop experiences
Experience / 05
Where I have worked
-
Research
Research Associate
Kharazmi University
23⁄9⁄2021
Remote Sensing Climate Extremes Hybrid Modelling Signal Processing Software Development -
Teaching
Programming & AI Lecturer
Shahid Beheshti University
18⁄10⁄2021
Programming, Graphic, Data Science, and AI courses.
Python Django Machine learning Deep Learning Data Science Photoshop AutoCad WordPress
Teaching / 06
Teaching the next generation of builders
Subjects I teach, mostly to engineers and scientists who need computation as a working tool.
Python
foundations → applied
Machine Learning
models · evaluation
Deep Learning
architectures · training
Artificial Intelligence
concepts · applications
Data Science
analysis · visualisation
Back End
web · databases
Programming
logic · structure
Scientific Computing
numerics · simulation
Front End
web · graphic
Adobbe Softwares
graphic
Skills / 07
A connected toolkit
Hover a node to see what it connects to...
- Python
- Machine Learning
- Deep Learning
- Signal Processing
- Remote Sensing
- TensorFlow
- Keras
- NumPy
- Pandas
- MATLAB
- Django
- JavaScript
- Google Earth Engine
- Git
- Linux
- Docker
- PostgreSQL
Stack / 08
Tools I reach for
Languages
Python
JavaScript
MATLAB
PHP
C#
AI & data
TensorFlow
Keras
Pandas
NumPy
Scikit-learn
Web
Django
Laravel
Node.js
Bootstrap
Scientific
Google
Earth Engine
SNAP
OriginLab
Infrastructure
Git
GitHub
Linux
Docker
Education / 09
Academic background
Master of Science
Kharazmi University
2018 - 2021
Thesis:
Investigation of Capability of Velocity Signal
Filtering Methods for Turbulent Flow Rate,
Measured by Acoustic Doppler Velocimeter
Areas: water & hydraulic engineering & signal processing
Open source / 11
Building in public
Research code belongs in the open. These are the tools I maintain and release.
Turning complex physical systems into understandable data, models, and decisions.
Interested in working together?
For research collaboration, teaching, scientific software, or computational modelling, I am glad to hear from you.
Let's build something meaningful.
Water × Climate × Data × AI × Science