An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA

Headwater wetlands provide many benefits such as water quality improvement, water storage, and providing habitat. These wetlands are characterized by water levels near the surface and respond rapidly to rainfall events. Driven by both groundwater and surface water inputs, water levels (WLs) can be above or below the ground at any given time depending on the season and climatic conditions. Therefore, WL predictions in headwater wetlands is a complex problem. In this study a hybrid modeling approach was developed for improved WL predictions in wetlands, by coupling a watershed model with artificial neural networks (ANNs). In this approach, baseflow and stormflow estimates from the watershed draining to a wetland are first estimated using an uncalibrated Soil and Water Assessment Tool (SWAT). These estimates are then combined with meteorological variables and are utilized as inputs to an ANN model for predicting daily WLs in wetlands. The hybrid model was used to successfully predict WLs in a headwater wetland in coastal Alabama, USA. The model was then used to predict the WLs at the study wetland from 1951 to 2005 to explore the possible teleconnections between the El Niño Southern Oscillation (ENSO) and WLs. Results show that both precipitation and the variations in WLs are partially affected by ENSO in the study area. A correlation analysis between seasonal precipitation and the Nino 3.4 Index suggests that winters are wetter during El Niño in Coastal Alabama. Analysis also revealed a significant negative correlation between WLs and the Nino 3.4 Index during the El Niño phase for spring. The findings of this study and the developed methodology/tools are useful to predict long-term WLs in wetlands and construct more accurate restoration plans under a variable climate

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

National surveillance of cancer survival in Iran (IRANCANSURV): Analysis of data of 15 cancer sites from nine population-based cancer registries

Cancer survival is a key indicator for the national cancer control programs. However, survival data in the East Mediterranean region (EMR) are limited. We designed a national cancer survival study based on population-based cancer registries (PBCRs) from nine provinces in Iran. The current study reports 5-year net survival of 15 cancers in Iranian adults (15-99 years) during 2014 to 2015 in nine provinces of Iran. We used data linkages between the cancer registries and the causes of death registry and vital statistics and active follow-up approaches to ascertain the vital status of the patients. Five-year net survival was estimated through the relative survival analysis. We applied the international cancer survival standard weights for age standardization. Five-year survival was highest for prostate cancer (74.9, 95 CI 73.0, 76.8), followed by breast (74.4, 95 CI 72.50, 76.3), bladder (70.4, 95 CI 69.0, 71.8) and cervix (65.2, 95 CI 60.5, 69.6). Survival was below 25 for cancers of the pancreas, lung, liver, stomach and esophagus. Iranian cancer patients experience a relatively poor prognosis as compared to those in high-income countries. Implementation of early detection programs and improving the quality of care are required to improve the cancer survival among Iranian patients. Further studies are needed to monitor the outcomes of cancer patients in Iran and other EMR countries. © 2022 UICC