Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture

Tayfur, Gokmen; Brocca, Luca, 2015, Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture, Water resources management (2015). doi_10.1007/s11269-015-1012-0,
URL: http://www.cnr.it/prodotto/i/332329

This study developed Mamdani-type fuzzy logic model to simulate daily discharge as a function of soil moisture measured at three different depths (10, 20 and 40 cm) and rainfall. The model was applied to 13 km2 size Colorso Basin in central Italy for a period from October 2002 to April 2004. For each variable of soil moisture, rainfall, and discharge, 9 fuzzy subsets were employed while 30 fuzzy rules, relating the input variables (soil moisture and rainfall) to the output variable (discharge), were optimized. The model employed the min inferencing, max composition, and the centroid method. The model application results revealed that Mamdani-type fuzzy logic model can be employed to incorporate soil moisture along with rainfall to simulate discharge. Using soil moisture measured at 40 cm soil depth along with rainfall produced better simulation of discharge with NS = 0.68 and R = 0.82. The performance of the model was also tested against a conceptual rainfall-runoff model of MISDc (Modello Idrologico Semi-Distribuito in continuo). MISDc couples an event-specific component with a module for continuous time soil water balance for taking into account the variable antecedent wetness conditions. The MISDc model requires estimation of seven parameters and the measurements of the hydrometeorological variables such as rainfall and air temperature. The comparative study revealed that fuzzy model performs better in capturing runoff peak rates and overall trend of high and small flooding events.

Data from https://intranet.cnr.it/people/