Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH honey badger algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm (SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90 percent. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.Comment: 65 pages, 10 figures, 5 table

Investigating the results of breastfeeding counseling in mothers referred to the breastfeeding counseling clinic of the health centers

Background: The rate of exclusive breastfeeding in Iran at the ages of 3 and 6 months is estimated to be 44% and 27%, respectively, which is still far from the optimal index of the World Health Organization until 2030 (70% in the first six months). In order to monitor breastfeeding counseling centers, which have been set up with the aim of promoting breastfeeding and supporting mothers who have problems with breastfeeding. Methods: This is a descriptive-retrospective study and all the mothers who were referred to the Health and Treatment Center No. 2 in Mashhad between April 2019 and March 2019 were examined. This health center has two active counseling clinics and the collection of information is based on documents registered in offices and computers. The inclusion criteria for study were not having breast diseases, having an infant child, being able to read and write and living in Mashhad city. The exclusion criteria were also the unwillingness to participate in the study and the newborn suffering from diseases that are incompatible with breastfeeding. Therefore, census sampling was done. The data were analyzed using SPSS 16 software and the significance level was less than 0.05. Results: In 42.8% of cases, mothers had started feeding combined formula with breast milk before visiting, and in 28.1% of cases, when referring to counseling milk clinics, it was reported that the baby was fed only with formula. After breastfeeding consultations and follow-ups at the end of six months, the rate of exclusive breastfeeding is 34.1% (12% increase compared to the initial reference) and combined formula feeding with breastmilk is 27.8% (a 22% decrease compared to the first visit). And feeding with powdered milk alone was calculated to be 36.5% (an increase of 8.4% compared to the first visit). Conclusion: The positive role of breastfeeding counseling center in reducing the cases of artificial feeding is clear, and it is recommended to prepare written instructions based on the challenges in breastfeeding for breastfeeding counseling in the first month after delivery

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH- honey badger algorithm (HBA), GMDH- rat swarm algorithm (RSOA), GMDH- sine cosine algorithm (SCA), and GMDH- particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash–Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90%. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins