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These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country's environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation ( ), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

ObjectivesThere are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions.DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission.ResultsThe risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00).ConclusionsWe have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

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Objective Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (-0.45 h, 95% CI -0.52 to -0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

Drought is a complex natural hazard. Its several adverse impacts are prevailing in almost all climatic zones around the world. In this regards, drought monitoring and forecasting play a vital role in making drought mitigation policies. Therefore, several drought monitoring tools based on probabilistic models had been developed for precise and accurate inferences of drought severity and its effects. However, risk of inaccurate determination of drought classes always exists in probabilistic models. To overcome this issue, we proposed a new system based Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for three multi-scalar drought indices, namely Standardized Precipitation Index (SPI), Standardized Precipitation Temperature Index (SPTI), and Standardized Precipitation Evapotranspiration Index (SPEI) at one-month time scale. By the basic assumption of the Markov chain, the PWJADI is based on the temporal switched weights that are propagated from the transition probability matrix of each temporal classification of drought index. Application of the proposed method is made for three meteorological stations of Pakistan. We found that our proposed model has ability to restructure the drought classes by capturing and bending the information from the historical behaviour of each drought class. Consequently, to make accurate and precise drought mitigation policies, the proposed method may integrate into effective drought monitoring systems.