By Prof. Dr. Saud Altaf
Can artificial intelligence really predict environmental change? The short answer is increasingly yes, although not for everything and never with absolute certainty. Over recent years, AI systems have moved from laboratory pilots to frontline tools that forecast weather, flag flood risks and anticipate heatwaves faster and, in many cases, more accurately than traditional methods. For a country like Pakistan, where livelihoods hinge on the timing of the monsoon and where communities from Gwadar to Gilgit face climate amplified hazards, this shift matters.
Modern AI weather models learn from vast archives of global observations and simulations to produce forecasts in minutes instead of hours. Their advantage is speed and scale. They can generate many scenarios quickly, which gives forecasters more time to interpret risks and communicate them to the public. Medium range predictions for three to ten days ahead are improving, and short term rainfall nowcasts are getting crisper at the neighborhood level. Faster updates translate into earlier, potentially lifesaving warnings when rivers are rising or heat indices are poised to jump.
These capabilities are already filtering into public facing tools. AI assisted flood platforms use river gauge data, satellite imagery and local topography to estimate where floodwaters might go and when. In districts along the Indus and its tributaries, where flash floods can arrive overnight, earlier guidance can help authorities’ pre-position relief supplies and communities plan evacuation routes. In coastal towns, machine learning models that blend ocean and weather data can offer better lead time for storm surge, while heat early warning systems can help municipalities adjust work hours, open cooling shelters and manage hospital readiness.
AI is also sharpening air quality forecasts by fusing emissions inventories, meteorological data and satellite observations. For farmers, researchers are building advisory systems that predict pest outbreaks and optimal irrigation windows by combining field sensors with climate signals. Importantly, the global meteorological community is not replacing physics based science with black boxes. The direction of travel is hybrid, where statistical learning accelerates calculations and fills gaps, while physical models and expert judgment remain the backbone of official warnings.
There are clear limits that no algorithm can erase. Earthquakes remain beyond prediction. Science can map hazard and estimate probabilities, but no system can tell us the exact date, location and magnitude of the next major quake. Seasonal monsoon performance can be indicated weeks to months ahead, yet year to year variability and complex ocean atmosphere interactions keep uncertainty high. Long term climate projections still depend on emissions pathways and large ensembles of physical models, which AI can complement but not replace.
For Pakistan, the path to trustworthy AI enabled prediction begins with data. Dense, well maintained weather stations, river gauges, coastal buoys and clean archives are the raw ingredients that make models useful. Open data sharing between national agencies and universities would let local researchers tune global AI to monsoon microclimates, from sea breeze interactions in Karachi to orographic storms in Khyber Pakhtunkhwa. Every deployment should pair transparency about uncertainty with routine human oversight and public education.
So can AI predict environmental change? It can, to a growing and often impressive degree, especially for weather and hydrology on timescales that matter for daily decisions. Think of AI as a force multiplier for Pakistani forecasters and planners, accelerating science, widening access to timely warnings and buying precious hours before the next storm, surge or heatwave arrives, provided we invest in data, institutions and public trust.
