You wouldn’t know it from the images of the Philippines this past week, but the science of disaster prediction is flourishing. Prediction and preparation are not the same thing, however, and questions have arisen about whether sufficient preparations were made for Typhoon Haiyan. These questions are posed after every disaster, and invariably there is only one answer: we could have prepared better. The Philippines could have been better prepared, but the best preparation is no match for two-hundred-mile-per-hour winds.

Nevertheless, our knowledge of how disasters occur, and how they will occur in the future, has never been more sophisticated. We are now able to prophesy impending cataclysms with a specificity that would have been inconceivable just several years ago. Several factors have contributed to this progress: a growing public anxiety about disasters; advances in disciplines as disparate as computer science, fluid mechanics, and neuroscience; and an infusion of funding from governments, universities, and especially corporations, which have figured out that disaster planning saves money in the long run. But the field remains in its infancy. Disaster prediction—like disaster science, disaster economics, disaster-response technology, disaster art, disaster cinema, disaster lit—is a growth industry. All indications suggest a growth curve that will continue to steepen well into the next century. Disaster is big business, and its prophets will profit.

Milestones in the past year include the March publication, by a team of U.C.L.A. scientists, of a new computer model that predicts where the next global pandemic will originate. The model gives the most favorable odds to Egypt’s Nile Delta and several areas in coastal and central China. One of these places is the most likely site for a particularly virulent strain of bird flu to jump species to human beings, creating a globetrotting virus that would kill more than six million Americans, according to a 2009 Department of Defense report that was declassified in September. In June, at a conference in Brisbane, an emergency-management specialist explained that Geographic Information Systems technology—which collects exhaustive data on land elevation, the condition of roads and buildings, water levels, population density, and meteorology—will help governments prepare for a flood years before it occurs. The technology will be used to determine when a flood is likely to happen, whether your house will be submerged, and whether you will survive if you don’t evacuate.

In the greater Dallas metropolitan area, engineers from the University of Massachusetts have been installing the prototype of a new tornado-detection radar system that should allow scientists to detect tornadoes twenty-five per cent faster than old models. In October, researchers at Purdue University filed a patent that will allow food-processing plants to screen for salmonella within a single six-hour work shift, much faster than current methods, which can take more than a day (by which point the tainted meat may have already reached supermarkets). And just over two weeks ago, a team of British and Australian neuroscientists published an algorithm that they believe may predict stock-market crashes.

Most of the progress in this field, to be clear, has been modest, incremental, and uncertain. Our current tornado-tracking models indicate the presence of a tornado sixteen minutes before it forms; the radar system currently being tested in Dallas would increase the warning time by only four minutes. ShakeAlert, an app recently developed by a U.C. Berkeley seismologist, makes funny noises whenever it detects an earthquake. Unfortunately, it cannot predict a major earthquake more than a minute before it hits—roughly enough time to duck under the nearest table and curl into the fetal position.

The science of typhoon prediction also has its blind spots—in the case of Haiyan, the blind spot was forty-six nautical miles in radius. The storm was first detected as a tropical depression a full five days before it made landfall. But this information was relatively useless, because the storm’s intensity could not be accurately predicted until hours before Haiyan passed over the southern tip of Eastern Samar province. Earlier forecasts had predicted a storm with an intensity between Category 1 and Category 5—the lowest and highest hurricane classifications available. This is like a doctor telling you that your cough could be a symptom of a seasonal cold or lung cancer.

But the science will improve. Though prediction abilities vary depending on the type of disaster (volcanologists have it much tougher than meteorologists), many of the current inefficiencies can be attributed to limitations of financial cost, computer processing power, and the complexity of analyzing vast amounts of data—all problems that will be overcome with time. As Alan Boyle of NBC News has pointed out, scientists would have been able to make far better predictions about Typhoon Haiyan had more money been invested in detection. No government, for instance, was able to send typhoon-hunting airplanes or drones into the storm to make accurate measurements of wind speeds and barometric pressure. The United States once had a program in the South Pacific charged with doing exactly this—the Fifty-fourth Weather Reconnaissance Squadron—but President Reagan discontinued the “Typhoon Chasers” in 1987.

Disaster predictions will become more accurate, but what difference will it make? Will it save lives, or even change behavior? Tokyo’s nine million residents are today fully aware that their city is due for a massive earthquake; last year, the University of Tokyo’s Earthquake Research Institute calculated that there is a seventy per cent likelihood that an earthquake with a magnitude of 7.0 or higher will hit the city by 2016. A 7.0 earthquake would kill eleven thousand people and cost Japan a trillion dollars. Angelenos who continue to buy multi-million-dollar houses on the escarpment of the San Gabriel Mountains—”on the steep slopes where the subdivisions stop and the brush begins” —know that the mountains want nothing more than to slide into the sea. Phoenix knows that it’s running out of water, Miami knows that it’s sinking into the Atlantic Ocean, and Seattle knows that it’s due for a reprise of the 9.0 megathrust earthquake that hit in 1700.

Anybody who had bothered to read the 1995 Metro New York Hurricane Transportation Study issued by the Army Corps of Engineers, or historical accounts of previous hurricanes that hit New York City—such as the 1821 storm that caused the East River to meet the Hudson in what is now SoHo—would have been prepared for the extent of Hurricane Sandy’s destruction. We also already know how much more severe the destruction will be when a stronger storm hits. Sandy was no longer a classified as a hurricane when it made landfall on the New Jersey coast, but a post-tropical cyclone; a Category 3 hurricane hit New York as recently as 1938.

And the Philippines had no delusions about the risks posed by typhoons. It had plenty of experience to draw from: the nation endures an average of twenty typhoons annually. Nobody knows exactly when the city of Tacloban—one of the areas hardest hit by Haiyan—first came into existence, because the city’s original municipal records were destroyed in a typhoon. The six most destructive examples in recorded history have all occurred since 1990, and average storm intensity and sea level continue to rise every year. In May, the executive director of the Philippines’ National Disaster Risk Reduction and Management Council acknowledged that global warming posed an “existential threat” to the nation.