Costing Improved Water Supply Systems for Low-income Communities

This manual and the free downloadable costing tool is the outcome of a project identified by the Water, Sanitation and Health Programme (WSH) of the World Health Organization (WHO) faced with the challenge of costing options for improved access, both to safe drinking water and to adequate sanitation. Although limited in scope to the process of costing safe water supply technologies, a proper use of this material lies within a larger setting considering the cultural, environmental, institutional, political and social conditions that should be used by policy decision makers in developing countries to promote sustainable development strategies.  Costing Improved Water Supply Systems for Low-income Communities provides practical guidance to facilitate and standardize the implementation of social life-cycle costing to “improved” drinking-water supply technologies. These technologies have been defined by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation, as those that, by the nature of its construction, adequately protect the source of water from outside contamination, in particular with faecal matter. The conceptual framework used has also been conceived to be applied to costing improved sanitation options.  To facilitate the application of the costing method to actual projects, a basic tool was developed using Microsoft Excel, which is called a water supply costing processor. It enables a user-friendly implementation of all the tasks involved in a social life-cycle costing process and provides both the detailed and the consolidated cost figures that are needed by decision-makers. The scope and the limits of the costing method in a real setting was assessed through field tests designed and performed by local practitioners in selected countries. These tests were carried out in Peru and in six countries in the WHO regions of South-East Asia and the Western Pacific. They identified practical issues in using the manual and the water supply costing processor and provided practical recommendations

Household Water Demand Estimation using Micro-Level Data

La Reunion Island is facing to specific difficulties in supplying drinking water. In the western part of this french departement drincking water is lacking. In this context, well-suited policies require to know perfectly households consumption behavior. To this end, the household water demand function is estimated. Data have been recorded on a random sample of 2000 consumers by a telephone poll. Water bills have been collected by post, from 200 volunteer households.

Econometric Modeling and Analysis of Residential Water Demand Based on Unbalanced Panel Data

This paper develops an econometric methodology devised to analyze a sample of time unbalanced panel data on residential water consumption in the French island La Reunion with the purpose to bring out the main determinants of household water consumption and estimate the importance of water consumption by uses. For this purpose, we specify a daily panel econometric model and derive, by performing a time aggregation, a general linear regression model accounting for water consumption data recorded on periods of any calendar date and time length. To esti-mate efficiently the parameters of this model we develop a feasible two step generalized least square method. Using the principle of best linear unbiased prediction, we finally develop an approach allowing to consistently break down the volume of water consumption recorded on household water bills by uses, namely by enforcing this estimated decomposition to add up to the observed total. The application of this methodology to a sample of 437 unbalanced panel observations shows the scope of this approach for the empirical analysis of actual data.econometric modeling; water consumption; panel data

Probabilistic Safety Regions Via Finite Families of Scalable Classifiers

Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. The data analyst may minimize the classification error on a class at the expense of increasing the error of the other classes. The error control of such a design phase is often done in a heuristic manner. In this context, it is key to develop theoretical foundations capable of providing probabilistic certifications to the obtained classifiers. In this perspective, we introduce the concept of probabilistic safety region to describe a subset of the input space in which the number of misclassified instances is probabilistically controlled. The notion of scalable classifiers is then exploited to link the tuning of machine learning with error control. Several tests corroborate the approach. They are provided through synthetic data in order to highlight all the steps involved, as well as through a smart mobility application.Comment: 13 pages, 4 figures, 1 table, submitted to IEEE TNNL

Estimation de la demande d'eau potable à La Réunion sur données d'enquête

L'article présente et exploite une enquête réalisée auprès de 2000 ménages, par nos soins à la Réunion. L'objectif est d'analyser les comportements de consommation d'eau potable afin de réduire les excès de consommation domestique. On estime pour cela la fonction de demande d'eau. On montre ainsi qu'une politique de prix, transitant via un effet revenu virtuel peut être préconisée pour les bas revenus. Cette politique pourra être complétée par une campagne de prévention auprès des ménages habitant une maison située dans les zones climatiques les plus exposées au manque d'eau.demande d'eau potable, sondage, variables instrumentales, données manquantes

CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence

Everyday life is increasingly influenced by artificial intelligence, and there is no question that machine learning algorithms must be designed to be reliable and trustworthy for everyone. Specifically, computer scientists consider an artificial intelligence system safe and trustworthy if it fulfills five pillars: explainability, robustness, transparency, fairness, and privacy. In addition to these five, we propose a sixth fundamental aspect: conformity, that is, the probabilistic assurance that the system will behave as the machine learner expects. In this paper, we propose a methodology to link conformal prediction with explainable machine learning by defining CONFIDERAI, a new score function for rule-based models that leverages both rules predictive ability and points geometrical position within rules boundaries. We also address the problem of defining regions in the feature space where conformal guarantees are satisfied by exploiting techniques to control the number of non-conformal samples in conformal regions based on support vector data description (SVDD). The overall methodology is tested with promising results on benchmark and real datasets, such as DNS tunneling detection or cardiovascular disease prediction.Comment: 12 pages, 7 figures, 1 algorithm, international journa

Décomposition d'agrégats au moyen d'indicateurs. Une méthode économétrique

In this paper, we develop a method allowing to split up an aggregate according to its homogeneous components, using statistical indicators. This problem is formulated as one of optimal linear forecast in a generalized linear regression model structured in order to explain the time path of an heterogeneous aggregate. This formulation leads to a consistent breakdown of such an aggregate enabling the measurement of the statistical estimation error of the decomposition.

Costing Improved Water Supply Systems for Low-income Communities

This manual and the free downloadable costing tool is the outcome of a project identified by the Water, Sanitation and Health Programme (WSH) of the World Health Organization (WHO) faced with the challenge of costing options for improved access, both to safe drinking water and to adequate sanitation. Although limited in scope to the process of costing safe water supply technologies, a proper use of this material lies within a larger setting considering the cultural, environmental, institutional, political and social conditions that should be used by policy decision makers in developing countries to promote sustainable development strategies.  Costing Improved Water Supply Systems for Low-income Communities provides practical guidance to facilitate and standardize the implementation of social life-cycle costing to “improved” drinking-water supply technologies. These technologies have been defined by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation, as those that, by the nature of its construction, adequately protect the source of water from outside contamination, in particular with faecal matter. The conceptual framework used has also been conceived to be applied to costing improved sanitation options.  To facilitate the application of the costing method to actual projects, a basic tool was developed using Microsoft Excel, which is called a water supply costing processor. It enables a user-friendly implementation of all the tasks involved in a social life-cycle costing process and provides both the detailed and the consolidated cost figures that are needed by decision-makers. The scope and the limits of the costing method in a real setting was assessed through field tests designed and performed by local practitioners in selected countries. These tests were carried out in Peru and in six countries in the WHO regions of South-East Asia and the Western Pacific. They identified practical issues in using the manual and the water supply costing processor and provided practical recommendations

On the use of the Box-Cox transformation in censored and truncated regression models

International audienceIn this paper we revisit some issues related to the use of the Box-Cox transformation in censored and truncated regression models, which have been overlooked by the econometric and statistical literature. We first analyze the shape of the density function of the random variable which, rescaled by a Box-Cox transformation, leads to a normal random variable. Then, we identify the value ranges of the Box-Cox scale parameter for which a regular expectation of the derived random variable does not exist. This result calls for an extension of the concept of expectation, which can be computed regardless of the value of the scale parameter. For this purpose, we extend the concept of mean of a rescaled series of observations to the case of a random variable. Finally, we run estimates of censored and truncated Box-Cox standard Tobit models to determine the range of the scale parameter most relevant for empirical demand analyzes. These estimates highlight significant deviations from the assumption of normality of the dependent variable towards highly right skewed and leptokurtic distributions with no expectation