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Suspended sediment load is an indicator of erosion in watersheds. Therefore, accurately estimating the daily suspended sediment load (DSSL) is an important issue in river engineering. In this research, Artificial Neural Networks (ANN), Genetic Expression Programming (GEP) intelligent models, and the traditional Sediment rating curve (SRC) model were used to estimate DSSL in the Kasilian and Rood Zard watersheds in Iran. The input data to the models included instantaneous flow discharge (Q), average daily flow discharge (Qi), average daily flow discharge with a delay of three days (Qi-1,Qi-2,Qi-3), average daily precipitation (Pi), and average daily precipitation with a delay of three days (Pi-1,Pi-2,Pi-3); the output data was DSSL. In this research, the selforganizing map (SOM) artificial neural network was used for data clustering, and gamma test (GT) methods were used to obtain the best combination of input variables to intelligent models. The results showed that the best models for estimating DSSL in the Kasilian and Rood Zard watersheds were respectively the ANN model with an activation function of tangent sigmoid with the best combination of input variables (Qi-1,Qi-2,Qi-3,Pi,Pi-1,Pi-2,Pi-3) and the GEP model with the input variables Qi,Qi-1,Qi-2,Pi,Pi-1,Pi-2,Pi-3. The statistical values of the ANN-tangent sigmoid model for the Kasilian watershed were MAE=231.4 (ton day-1), RMSE=578.6 (ton day-1), NSE =0.98, and R²=0.98; these values for the GEP model in the Rood Zard watershed were MAE=475.7 (ton day-1), RMSE=1671.9 (ton day-1), NSE=0.99, and R²=0.99. The SRC model in the Kasilian watershed with R²=0.34 and NSE=0.08 and the Rood Zard watershed with R²=0.59 and NSE=-0.11 showed the low accuracy of this model in estimating DSSL. [ABSTRACT FROM AUTHOR]

Background: Insufficient physical activity (PA) is a major risk factor for non-communicable diseases (NCDs) and one of the leading causes of premature mortality worldwide. This study examined the prevalence and independent determinants of insufficient PA among adults resident of Tehran utilizing Tehran Cohort Study Data (TeCS)., Method: We used the recruitment phase data from the TeCS with complete data on PA. PA was assessed through a Likert-scaled question and categorized into three groups. Utilizing data from the 2016 national census, the age- and sex-weighted prevalence of insufficient PA in Tehran was determined. The adjusted logistic regression model is used to neutralize influencing factors and determine the factors associated with insufficient PA., Result: The weighted prevalence of insufficient PA was 16.9% among the 8213 adult citizens of Tehran, with a greater prevalence among females (19.0% vs. 14.8% among males). Additionally, older age groups, unemployed, housewives, and illiterate educated participants displayed a much higher prevalence of insufficient PA (p < 0.001). Moreover, Tehran's central and southern districts had higher rates of insufficient PA. Concerning the adjusted regression model, older age (Odds ratio [OR]: 4.26, 95% confidence interval [95% CI]: 3.24-5.60, p < 0.001), a lower education level (p < 0.001), unemployment (OR: 1.80, 95% CI: 1.28-2.55, p = 0.001), being a housewife (OR: 1.44, 95% CI: 1.15-1.80, p = 0.002), higher body mass index (BMI) (OR for BMI > 30: 1.85, 95% CI: 1.56-2.18, p < 0.001), opium consumption (OR: 1.92, 95% CI: 1.46-2.52, p < 0.001), diabetes mellitus (OR: 1.25, 95% CI: 1.06-1.48, p = 0.008), hypertension (OR: 1.29, 95% CI: 1.11-1.50, p = 0.001), and coronary artery diseases (OR: 1.30, 95% CI: 1.05-1.61, p = 0.018), were significantly associated with insufficient PA., Conclusions: The identified associated factors serve as a valuable guide for policymakers in developing tailored intervention strategies to address the needs of high-risk populations, particularly among older adults and females., (© 2024. The Author(s).)