Abstract: The algalbloom prediction models are constructed by using the decision trees to qualitatively predict bloom timing and use the nonlinear piecewise regression to quantitatively predict bloom intensity.The traffic light systems are used as the indicator for degree of algal bloom.Liuhai-lake in Beijing city is used an example.The constructed model indicates that the water inflow,the temperature and total phosphorus are the most impact factors on the algalbloom in Liuhai-lake.The concentration of Chl-a<30 μg/L is labeled as green,30 μg/L60 μg/L as red.When water inflow Q>79.0×10⁴m³ or Q<79.0×10⁴m³ and water temperature<13.4℃,the indicator is green;when Q<79.0×10⁴m³, water temperature T>13.4℃,the indicator is yellow;and when Q<38.7×10⁴m³,water temperature>23.25℃,TP>0.13 μg/L,the indicator is red.The model is test by an independent dataset from the same area,the predicted blooming time error rate and the error of predicted bloom intensity are presented in the paper.The model has great advantages to deal with the common problem in algal-blooms.It's more accurate when the limiting factor is changing.And the structure is understandable and easy to interpret.