Similarity Based Neuro-fuzzy System for Rainfall-runoff Modeling in an Urban Tropical Catchment

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Real Time Neural Fuzzy System for Rainfall-Runoff Modeling

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Entrainment and mixing layer oscillations induced by a flow beneath a rectangular compartment

The present study investigates the entrainment characteristics and feedback mechanisms causing periodic mixing layer oscillations associated with a flow stream beneath a rectangular compartment. The entrainment studies were based on mixing rate measurements in rectangular and circular storage tanks from two previous studies. An approximate unified equation for predicting the entrainment rate across a density interface, in both rectangular and circular storage tanks, induced by a flow stream below the tank, was derived. The results show that under equivalent conditions, the mixing rates in a circular tank is higher than in a rectangular tank, probably an indication of more complex 3-D flow patterns inside a circular tank as compared to the 2-D flow patterns inside a rectangular tank.Doctor of Philosophy (CSE

Mixing between sea and fresh water layers in a floating storage tank with a concentric bottom opening

An investigation was made to study the rate of mixing between fresh and sea water layers in a cylindrical storage tank, with a concentric bottom opening, floating in a large body of sea water. Mixing as a result of both diffusion and entrainment under quiescent state and turbulent flow conditions were considered.Master of Engineerin

The data-driven approach as an operational real-time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data-driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People’s Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four- and five-lead-day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto- and cross-correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi-step-ahead error prediction was superior to the fully recursive procedure. The RAR-based partial recursive updating procedure significantly improved three-, four- and five-lead-day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR-based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m