Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m3 s−1 and 0.81, 2.297 m3 s−1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m3 s−1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting

Different effects of energy dependent irradiation of red and green lights on proliferation of human umbilical cord matrix-derived mesenchymal cells

Light-emitting diodes (LED) have recently been introduced as a potential factor for proliferation of various cell types in vitro. Nowadays, stem cells are widely used in regenerative medicine. Human umbilical cord matrix-derived mesenchymal (hUCM) cells can be more easily isolated and cultured than adult mesenchymal stem cells. The aim of this study was to evaluate the effect of red and green lights produced by LED on the proliferation of hUCM cells. hUCM cells were isolated from the umbilical cord, and light irradiation was applied at radiation energies of 0.318, 0.636, 0.954, 1.59, 3.18, 6.36, 9.54, and 12.72 J/cm2. Irradiation of the hUCM cells shows a significant (p < 0.05) increase in cell number as compared to controls after 40 h. In addition, cell proliferation on days 7, 14, and 21 in irradiated groups were significantly (p < 0.001) higher than that in the non-irradiated groups. The present study clearly demonstrates the ability of red and green lights irradiation to promote proliferation of hUCM cells in vitro. The energy applied to the cells through LED irradiation is an effective factor with paradoxical alterations. Green light inserted a much profound effect at special dosages than red light

Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models

Reliable and accurate modeling of groundwater quality is an important element of sustainable groundwater management of productive aquifers. In this research, specific conductance (SC) of groundwater is predicted based on different individual and integrative machine learning, adaptive neuro-fuzzy inference system (ANFIS), and nonlinear mathematical models. For developing the integrative models, the well-known particle swarm optimization (PSO) and novel manta ray foraging optimization (MRFO) heuristic algorithms are embedded in the models. Presenting different univariate, bivariate, and multivariate input scenarios, the parameters used to develop and validate the models include groundwater level, salinity, and water temperature at an observation well near Florida City. The findings reveal that applying more independent parameters (multivariate scenario) enhances the performance of both the mathematical and machine learning models. Even though the mathematical models present an acceptable performance for the prediction of SC (index of agreement, IA, equals 0.933), the ANFIS models provide the most accurate SC predictions (IA = 0.943). Both the PSO and MRFO algorithms improved the prediction capability of the ANFIS models with, respectively, 13% and 5% for the RMSE