Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach

This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the interpretability of statistical methods. For this reason, a random forest model is used to predict daily demands 1 day ahead for groups of properties (mean of 3.8 households/group) with homogenous characteristics. A variety of interpretable machine learning techniques [variable permutation, accumulated local effects (ALE) plots, and individual conditional expectation (ICE) curves] are used to quantify the influence of these predictors (temporal, weather, and household characteristics) on water consumption. Results show that when past consumption data are available, they are the most important explanatory factor. However, when they are not, a combination of household and temporal characteristics can be used to produce a credible model with similar forecasting accuracy. Weather input has overall a mild to no effect on the model's output, although this effect can become significant under certain conditions. </p

Water demand forecasting accuracy and influencing factors at different spatial scales using a Gradient Boosting Machine

Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode locations, household characteristics, and weather data in order to identify the relationships between spatial scale, influencing factors, and forecasting accuracy. For this purpose, a Gradient Boosting Machine (GBM) is used to predict water demand 1‐7 days into the future. Results show an exponential decay in prediction accuracy from a Mean Absolute Percentage Error (MAPE) of 3.2% to 17%, for a reduction in group size from 600 to 5 households. Adding explanatory variables to the forecasting model reduces the MAPE up to 20% for the peak days and smaller household groups (20‐56 households), whereas for larger aggregations of properties (100‐804 households), the range of improvement is much smaller (up to 1.2%). Results also show that certain types of input variables (past consumption and household characteristics) become more important for smaller aggregations of properties whereas others (weather data) become less important

Comparative evaluation of group-based mindfulness-based stress reduction and cognitive behavioral therapy for the treatment and management of chronic pain disorders: protocol for a systematic review and meta-analysis with indirect comparisons

Abstract Background Chronic pain disorders impact the physical, psychological, social, and financial well-being of between 10%–30% of Canadians. The primary aims of psychological interventions targeting chronic pain disorders are to reduce patients’ pain-related disability and to improve their quality of life. Cognitive behavioral therapy (CBT) is the prevailing treatment for chronic pain, however mindfulness-based stress reduction (MBSR) has displayed promise as an alternative treatment option. The objective of this systematic review and meta-analysis is to compare MBSR to CBT in their relative ability to reduce pain-related disability and intensity, to alleviate emotional distress, and to improve global functioning in chronic pain patients. Methods/design We will conduct a systematic review with meta-analyses to compare MBSR to CBT in the treatment of chronic pain disorders in adults. We will report our review according to the recommendations provided by the PRISMA statement. Randomized studies will be included and the literature search will comprise Ovid MEDLINE®, Ovid MEDLINE® In-Process & Other Non-Indexed Citations, Embase Classic + Embase, PsycINFO, the Cochrane Library on Wiley, including CENTRAL, Cochrane Database of Systematic Reviews, DARE, and HTA. Study selection and data extraction will be conducted by independent investigators and in duplicate. Outcomes of interest will include pain interference, pain intensity, emotional functioning, and patient global impression of change. The Cochrane risk of bias tool will be used to assess risk of bias of included studies. As we anticipate that scales used to measure participant responses will be related but varied from study to study, standardized mean differences will be used to compare effect sizes between treatment modalities. Given the possibility of little or no head-to-head evidence comparing MBSR with CBT, we will use indirect treatment comparison methodology to assess the relative effectiveness of these interventions. Discussion The findings from this study will assist patients and treatment providers to make informed decisions regarding evidence-based treatment selection for chronic pain disorders. Systematic review registration PROSPERO CRD4201400935

An investigation of the evidence of benefits from climate compatible development

Climate change is likely to have profound effects on developing countries both through the climate impacts experienced, but also through the policies, programmes and projects adopted to address climate change. Climate change mitigation (actions taken to reduce the extent of climate change), adaptation (actions taken to ameliorate the impacts), and on-going development are all critical to reduce current and future losses associated with climate change, and to harness gains. In the context of limited resources to invest in climate change, policies, programmes, or projects that deliver ‘triple wins’ (i.e. generating climate adaptation, mitigation and development benefits) – also known as climate compatible development – are increasingly discussed by bilateral and multilateral donors. Yet there remains an absence of empirical evidence of the benefits and costs of triple win policies. The purpose of this paper is therefore to assess evidence of ‘triple wins’ on the ground, and the feasibility of triple wins that do not generate negative impacts. We describe the theoretical linkages that exist between adaptation, mitigation and development, as well as the trade-offs and synergies that might exist between them. Using four developing country studies, we make a simple assessment of the extent of climate compatible development policy in practice through the lens of ‘no-regrets’, ‘low regrets’ and ‘with regrets’ decision making. The lack of evidence of either policy or practice of triple wins significantly limits the capacity of donors to identify, monitor or evaluate ‘triple wins at this point in time. We recommend a more strategic assessment of the distributional and financial implications of 'triple wins' policies

Smart Water Demand Forecasting: Learning from the Data

Accurate forecasts of demand are essential for water utilities in order to manage, plan, and optimize the operation of their network. This work aims to develop a new method for short- term water demand forecasting by utilizing a new data-driven approach based on Random Forests, as well as consumption recordings, household, and socio-economic characteristics, and weather data. Initial results, obtained on real-life consumption data from the UK, demonstrate the potential of this method and show the importance of disaggregating consumption when attempting to determine the influence of weather on water demand. In this study, adding weather input to the model achieved improved forecasting accuracy, especially for the aggregation of properties with medium occupancy and affluent residents during summer months

A promising Start? The Local Network Fund for Children and Young People: Interim Findings from the National Evaluation

This is a summary of the interim evaluation report of the National Evaluation of the Local Network Fund (LNF) for Children and Young People. It is based on data gathered during the first phase of the evaluation (between October 2002 to December 2003). A final report of the National Evaluation will be available early in 2005. A consortium of research organisations, led by the University of Hull and including BMRB Social Research, The University of York and the University of Sheffield were commissioned in August 2002 by the-then Children and Young People’s Unit (CYPU) to carry out the evaluation