Muhammad Furqan

08/09/2026

Water stress isn't just about how much water exists. It's about demand ((how much water need) vs. supply(how much water is available). Khyber Pakhtun Khwa has enougn water demand and supply while most of the areas in Balochistan province has less demand and supply of water.

check out from the link 👇
https://furqan303.github.io/Water-Supply-Demand/

05/09/2026

I built an end-to-end Dynamic Disaster Simulation & Evacuation Decision Support System for a localized natural disaster like rising floodwaters, spreading wildfire, or a chemical spill) hits a populated area. Most emergency response teams still rely on static maps, historical traffic assumptions, or fragmented demographic data. When wind direction shifts or panicked drivers bottleneck key arterial roads, traditional routing breaks down. Emergency responders or city planners can watch the threat dynamically expand on the map based on time intervals.

Standard GPS shortest-path algorithms fail during crises because they ignore panic congestion and sealed roads. So, they also draw evacuation zones, set up roadblocks, and pinpoint safe triage centers.

I’d love to hear thoughts from folks working in GIS, GovTech, disaster management, and spatial graph algorithms. How is your organization tackling real-time evacuation modeling? and How could I improve it further?

25/08/2026

Many regions suffer from droughts, floods, heatwaves, and land degradation, but decision-makers don't know which areas are at greatest risk.
I developed a GEE workflow that combines
NDVI (vegetation)
NDWI (water)
Land Surface Temperature
Rainfall
Elevation
Population

to create a Climate Risk Map.

Instead of just leaving the data as a static image or raw code, I built a web interface that make the risk layers as semi-transparent over a standard base map so that urban planners and policymakers can clearly see exactly which cities, roads, and communities fall into High, Medium, or Low-risk zones.

20/08/2026

09/08/2026

What if the next major flood is not a surprise—but something we could see coming?

With more frequent and severe weather events, including heavy rainstorms, heatwaves, and floods, identifying vulnerable regions is becoming more critical than ever for proactive disaster management.

I developed this Flood Susceptibility Map for Pakistan to visualize which areas could be more vulnerable to inundation. Using a Multi-Criteria Decision Analysis (MCDA) approach, I modeled flood susceptibility by weighting seven critical environmental factors: Annual Rainfall, Extreme Precipitation (99th percentile), Elevation, Slope, Distance to Rivers, Land Cover, and Soil Texture.

The entire workflow was carried out in Google Earth Engine, followed by styling and refinement in QGIS.

So, where should we be looking next?
Check it out from the link below, and I’d love to hear your thoughts.
https://code.earthengine.google.com/cf49167f81a7b8bb76cec8464a9aced2

07/02/2026

How UAE Has Two Mega Flag Carriers | Emirates vs Etihad Explained!

11/12/2025

Built a GIS automation tool for data preprocessing. We upload CSV, GeoJSON, and Shapefiles (in ZIP), and it fixes the data through AI. It works by detecting the file format (CSV, GeoJSON, or Shapefile in ZIP), collecting the headers and the first five rows, and sending them to the AI so it can understand the data pattern and apply fixes such as detecting and correcting wrong coordinates, converting mixed projections to the standard WGS84 (EPSG:4326), and removing duplicates while setting missing values to NULL or 0. It also plots the data on a map for visualization.

I used LangChain and the Gemini API for AI analysis.

The frontend is built with Streamlit, a Python library.

The backend is entirely in Python, using libraries such as NumPy, GeoPandas, and Shapely.

07/12/2025

why we don't see auroras in pakistan

01/12/2025

Why Shopkeepers Say Cheap and Good About USB