Adapting empirical equations to Lake Kinneret data by using three calibration methods

This paper examines whether the relationships between a number of characteristic limnological variables (suspended particulate matter, turbidity, Secchi depth, light attenuation, and chlorophyll a) determined for temperate lakes are consistent with the relationships found in Mediterranean lakes such as Lake Kinneret. We found that the use of published relationships between lake variables may lead to erroneous results when applied indiscriminately to other lake types. lied linear regression, nonlinear regression and genetic algorithm (GA) approaches in order to fit the relationships to Lake Kinneret data. We showed that linear equations, simple or multivariate that do not require transformation prior to calibration, can be calibrated by simple and quick methods such as linear regression. However, when linear transformation is required prior to calibration the GA calibration produced better results. In addition, we found that the nonlinear regression method yielded a weak prediction, while the GA led to more accurate results. itivity analysis of the GA operators’ values indicated that using a crossover probability of 80% produced the best prediction. Furthermore, since the GA sometimes converges to local optimum, it must operate for a number of runs. We found that in our case 20 GA runs provided the most robust results.