Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Implementation of Early Intervention Protocol in Australia for 'High Risk' Injured Workers is Associated with Fewer Lost Work Days Over 2 Years Than Usual (Stepped) Care

Purpose To evaluate whether a protocol for early intervention addressing the psychosocial risk factors for delayed return to work in workers with soft tissue injuries would achieve better long-term outcomes than usual (stepped) care. Methods The study used a controlled, non-randomised prospective design to compare two case management approaches. For the intervention condition, workers screened within 1-3 weeks of injury as being at high risk of delayed returned to work by the Örebro Musculoskeletal Pain Screening Questionnaire-short version (ÖMPSQ-SF) were offered psychological assessment and a comprehensive protocol to address the identified obstacles for return to work. Similarly identified injured workers in the control condition were managed under usual (stepped) care arrangements. Results At 2-year follow-up, the mean lost work days for the Intervention group was less than half that of the usual care group, their claim costs were 30% lower, as was the growth trajectory of their costs after 11 months. Conclusions The findings supported the hypothesis that brief psychological risk factor screening, combined with a protocol for active collaboration between key stakeholders to address identified psychological and workplace factors for delayed return to work, can achieve better return on investment than usual (stepped) care.

Correction to: Implementation of Early Intervention Protocol in Australia for 'High Risk' Injured Workers is Associated with Fewer Lost Work Days Over 2 Years Than Usual (Stepped) Care

The original version of this article unfortunately contained a spelling error in one of the co-authors's names. The family name of the co-author was incorrectly displayed as "James McCauley" instead of "James McAuley. The original article has been corrected.

Predicting Return to Work in a Heterogeneous Sample of Recently Injured Workers Using the Brief ÖMPSQ-SF

Purpose (1) to examine the ability of the Örebro Musculoskeletal Pain Screening Questionnaire-short version (ÖMPSQ-SF) to predict time to return to pre-injury work duties (PID) following a work-related soft tissue injury (regardless of body location); and (2) to examine the appropriateness of 50/100 as a suitable cut-off score for case identification. Methods Injured workers (IW) from six public hospitals in Sydney, Australia, who had taken medically-sanctioned time off work due to their injury, were recruited by insurance case managers within 5-15 days of their injury. Eligible participants (N = 213 in total) were administered the ÖMPSQ-SF over the telephone by the case manager. For objective (1) Cox proportional hazards regression analysis was used to predict days to return to PID using the ÖMPSQ-SF. For objective (2) receiver operator characteristic (ROC) analysis was used to determine the ÖMPSQ-SF total score that optimises sensitivity and specificity in detecting whether or not participants had returned to PID within 2-7 weeks. Results The total ÖMPSQ-SF score significantly predicted number of days to return to PID, such that for every 1-point increase in the total ÖMPSQ-SF score the predicted chance of returning to work reduced by 4% (i.e., hazard ratio = 0.96), p < 0.001. Sensitivity and specificity for the ROC analysis comparing ÖMPSQ-SF total score to return to PID within 2-7 weeks suggested 48 as the optimal cut off (sensitivity = 0.65, specificity = 0.79). Conclusion The results provide strong support for the use of the ÖMPSQ-SF in an applied setting for identifying those IW likely to have delayed RTW when administered within 15 days of the injury. While a score of 48/100 was the optimal cut point for sensitivity and specificity, pragmatically, 50/100 should be acceptable as a cut-off in future studies of this type.