Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Water Resources Management - Groundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high quality drinking water is a major...     

Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques

Water Resources Management - Improving drinking water source monitoring is crucial for efficiently managing the drinking water treatment process and ensuring the delivery of safe water. Data mining...     

A Rural Water Poverty Analysis in China Using the DPSIR-PLS Model

Water poverty assessment is a complex problem involving multiple systems such as water resources, the economy, society, and the environment. Thus, it is necessary to employ a comprehensive model comprising a set of interdisciplinary indicators that encompass water-related aspects, as well as a conceptual framework to organize all indicators and variables. Firstly, we develop water poverty indicators for rural areas of China by introducing the Driving forces–Pressures–States–Impacts–Responses (DPSIR) model, based on causality. Then, the indicator system and structural equation model (SEM) are combined, and the partial least squares approach to structural equation modeling (PLS-SEM) tests the hypothesis based on the causal relationship of the DPSIR model. Secondly, the weight of each variable is obtained using the modified model and structural equation software, and water poverty scores are calculated for each area. Exploratory spatial analysis of the results suggests that the distribution of water poverty in China’s rural areas displays low–low and high–high clustering. Policy makers should take advantage of the water resource situation in eastern and central regions to help the northwest, southwest, and southeast regions alleviate problems of water poverty.     

Quantification of the Driving Factors of Water Use in the Productive Sector Change Using Various Decomposition Methods

Water Resources Management - The water use in the productive sector in developing regions increases with quick socioeconomic development. This study is a quantitative analysis of the factors...     

Unravelling the Linkages Between Water, Sanitation, Hygiene and Rural Poverty: The WASH Poverty Index

Many studies have reported the effect of water supply, sanitation and hygiene (WASH) in improving health and ultimately alleviating poverty. Current coverage estimates show however that a large proportion of people in the world still do not have access to a simple pit latrine or a source of safe drinking water, and this situation worsens in rural areas. To help end these appalling figures, much effort has gone into the development of policy instruments which support decision-making, i.e. planning, targeting and prioritization. Indices and indicators are increasingly recognised as powerful tools for such purposes. This paper details the theoretical framework and development of a multidimensional, WASH-focused, thematic indicator: the WASH Poverty Index (WASH PI). It describes the methodology in index construction and disseminates achieved results in a variety of forms to promote the utility of the tool for the integrated analysis of WASH and poverty linkages. The article uses Kenya as initial case study to illustrate the application of the index. Overall, WASH PI helps identify priority areas and guide appropriate action and policy-making towards improved service delivery.     

Robust Methods for Identifying Optimal Reservoir Operation Strategies Using Deterministic and Stochastic Formulations

Water allocation in a competing environment is a major social and economic challenge especially in water stressed semi-arid regions. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. In this paper, two reservoir operation optimization models for water allocation to different users are presented. The objective functions of both models are based on the Nash Bargaining Theory which can incorporate the utility functions of the water users and the stakeholders as well as their relative authorities on the water allocation process. The first model is called GA–KNN (Genetic Algorithm–K Nearest Neighborhood) optimization model. In this model, in order to expedite the convergence process of GA, a KNN scheme for estimating initial solutions is used. Also KNN is utilized to develop the operating rules in each month based on the derived optimization results. The second model is called the Bayesian Stochastic GA (BSGA) optimization model. This model considers the joint probability distribution of inflow and its forecast to the reservoir. In this way, the intrinsic and forecast uncertainties of inflow to the reservoir are incorporated. In order to test the proposed models, they are applied to the Satarkhan reservoir system in the north-western part of Iran. The models have unique features in incorporating uncertainties, facilitating the convergence process of GA, and handling finer state variable discretization and utilizing reliability based utility functions for water user sectors. They are compared with the alternative models. Comparisons show the significant value of the proposed models in reservoir operation and supplying the demands of different water users.     

Identifying Homogeneous Water Quality Regions in the Nile River Using Multivariate Statistical Analysis

Detecting homogeneous regions in the Nile River is essential in carrying mathematical modelling. The aim of this paper is to indentify homogenous regions with respect to water quality. Eight years data were subjected to principal components analysis (PCA) to define the parameters responsible for the variability in water quality. The PCA produced three variates (or principal components). For the Nile stem, variates are related to bacterial pollution, organic pollution and then agricultural/nutrients. As for the Nile branches, variables group as coming from bacterial and organic sources, while the agricultural/nutrients stamp is more visible in summer. Then, cluster analysis (CA) was performed to verify whether the observations could be grouped into spatially coherent patterns. CA grouped sampling sites into three homogenous regions: upper, middle and lower Nile stem. To interpret the subdivision, CA was performed on municipal and demographic data coming from Nile governorates, such as potable water consumption, sewage collection, cultivated areas and population data. The cultivated areas group similarly to nutrients water quality data and the percentages of uncollected sewage group similarly to bacterial data. The consecutive use of PCA and CA enabled to determine the main sources of pollution and to identify homogeneous regions with respect to water quality variables.     

采煤沉陷水域水环境研究主要方法评述与趋势分析

采煤沉陷水域水环境系统具有相当的复杂性和特殊性,对其进行分析、评价、模拟与预测,是当前能源基地资源环境可持续发展关注的焦点。本文评述了采煤沉陷水域水环境研究的主要方法：调查分析与原始观测实验、水质评价方法和水质模型模拟预测,并指出研究方法应考虑沉陷水域的特殊性,分析了当前采煤沉陷水域水环境研究的主要问题与发展趋势,旨在为采煤沉陷区水环境的深入研究和规划管理提供参考。     

Mapping of Conjunctive Water Use Productivity Pattern in an Irrigation Command Using Temporal IRS WiFS Data

Irrigated agriculture in many areas of the world is currently being practiced from multiple water sources such as precipitation, canal, wetlands, ground aquifer, etc. This study highlights the use of high temporal remote sensing data [IRS-1D; Wide Field Sensor (WiFS), 188-m resolution] to assess conjunctive water use pattern and its productivity in the 6 Main Canal command of Damodar Irrigation Project West Bengal, India. In this command three sources of water (canal water, groundwater and wetland) were used for the rice growing system during the summer season. A multi-date (ten dates, two bands) image stack was prepared. Using this image stack and an unsupervised classification (Fuzzy k-means) backed by space–time spiral curve (ST-SCs) technique, canal release and wetlands information was used to prepare irrigated classes (canal, groundwater or wetlands) map for summer 2000. ST-SCs have been used to analyze temporal WiFS data to continuously monitor class dynamics over time and space and to determine class separability (different types of irrigated-classes) at various time periods within the season. Results showed that the area under agriculture, non-agriculture and water were 81%, 18.5% and 0.5%, of the total area respectively. While, groundwater, canal water and wetland irrigated rice were 67.6%, 25.6% and 6.8%, respectively out of the total agriculture area. Classification results found to have more than 89.3% overall accuracy for broad land cover, while sub-classes of rice i.e. irrigated classes found have reasonably good accuracy of 85.7%. A productivity index (LAI/water-requirement) was also developed. Productivity index was high for the wetland and groundwater irrigated rice as compared to the rice irrigated through canal water. These results were weighed against the observed yield data.     

Socio-economic Analysis of the Tonle Sap Region,Cambodia: Building Links and Capacity for Targeted Poverty Alleviation

Cambodia is recovering from three decades of violence. The Tonle Sap region is the poorest part of this deprived country. Its economy now suffers from substantial informal and destructive activities. A socio-economic analysis of the region--a part of the Mekong River Commission's Water Utilization Programme, which serves the lower Mekong's Basin Development Plan (BDP)--is documented. Besides the multifaceted collection and analysis of data and information from participatory fieldwork, various databases, mathematical models, expert interviews and so forth, a crucial part of the work is to build up and reinforce links between the Mekong River Commission, ministries, provincial authorities, non-governmental organizations, universities and ultimately local communities, and thereby root the BDP into the emerging civil society. Otherwise, the BDP is at high risk of sharing the fate of its predecessors on the Mekong as well as in many other parts of the world. This fate has too often been a failure of the plan, however integrated it has attempted to be.     

Water Allocation,Transfers and Conservation: Links between Policy and Hydrology

As the demand for water for agricultural, urban, industrial and environmental uses increases, it will be necessary to better understand the interaction between hydrology and the institutions that will govern water use. Water transfers are considered one of the primary tools for coping with increased water demand. This paper examines several key issues and demonstrates potential pitfalls that must be addressed when designing water transfer policy. An important result is that to be effective, water transfer policy must account for basin hydrology.     

农村供水监控数据共享交互方法研究

农村供水监控数据缺乏共享交互,不同水厂农村供水自动化监控系统往往各自为战,独立的系统、分散的数据很难向数字化、网络化、智能化的方向转变,需要花费较大的代价来实现数据共享交互。本研究对比当前流行的数据交互方法,选择Ｊｓｏｎ数据交互方法,研究定义了传感器、设备设施不同层级的数据指标,提出了设备设施定义、新增或更新传感器数据、视频安防监控数据等的数据结构,方便农村供水监控数据共享交互。该数据共享交互方法与传统方法相比,单个供水工程布设周期缩短50％以上,平均上行数据包大小缩小33％,破解了农村供水监控数据缺乏共享交互方法、已建系统数据共享费用高、维护成本高的难题。     

防洪调度及水资源管理数据挖掘系统研究

采用美国SAS数据挖掘软件,在对我国防洪调度及水资源管理需求详尽分析的基础上,探索了数据挖掘技术在防洪调度及水资源管理方面的应用理论、方法及技术;研究了商品化数据挖掘软件与防洪调度及水资源管理需求相结合的具体方法,建立了太湖流域和中央节点的防洪调度和水资源管理数据仓库系统;分析研究了数据应用的基本模式,提出了数据挖掘的主题,运用SAS数据挖掘套件建立了基于数据仓库的防洪调度及水资源管理数据挖掘系统。     

防洪调度及水资源管理数据挖掘系统研究

采用美国SAS数据挖掘软件,在对我国防洪调度及水资源管理需求详尽分析的基础上,探索了数据挖掘技术在防洪调度及水资源管理方面的应用理论、方法及技术;研究了商品化数据挖掘软件与防洪调度及水资源管理需求相结合的具体方法,建立了太湖流域和中央节点的防洪调度和水资源管理数据仓库系统;分析研究了数据应用的基本模式,提出了数据挖掘的主题,运用SAS数据挖掘套件建立了基于数据仓库的防洪调度及水资源管理数据挖掘系统.     

数据挖掘技术在水资源领域的应用方法研究

水资源数据具有量大、涉及面广、分布式、异构性等特点,采用传统的分析方法进行数据分析受到一定限制,而数据挖掘技术为充分利用水资源数据提供了有效手段,可应用于水资源管理、评价、预测、动态模拟等。水资源数据挖掘体系结构由数据层、组织层、挖掘层、决策层组成,其中数据层是基础,挖掘层是核心。水资源数据的数量和质量、挖掘方法的选择、挖掘结果的解释和评价是水资源数据挖掘的关键。     

基于数据挖掘技术的水资源管理指标分析

针对已构建的水资源管理指标体系中指标灵活性、可操作性不强,指标如何选取研究不足的问题,利用数据挖掘技术,找出指标之间的关系和数据中隐藏的知识,初步构建了水资源管理指标体系。以北京市为例对水资源管理数据进行属性子集选择,验证指标层选取的合理性并剔除无关属性;应用J48决策树算法,找出数据中隐藏的知识。该方法弥补了水资源管理指标如何选取方面研究的不足,为指标的选择和量化提供技术方法上的参考及依据。     

基于数据挖掘技术的水资源管理指标分析

针对已构建的水资源管理指标体系中指标灵活性、可操作性不强,指标如何选取研究不足的问题,利用数据挖掘技术,找出指标之间的关系和数据中隐藏的知识,初步构建了水资源管理指标体系.以北京市为例对水资源管理数据进行属性子集选择,验证指标层选取的合理性并剔除无关属性;应用J48决策树算法,找出数据中隐藏的知识.该方法弥补了水资源管理指标如何选取方面研究的不足,为指标的选择和量化提供技术方法上的参考及依据.     

Understanding Municipal Water Use and Data Availability: a Case Study across North Dakota,USA

Water Resources Management - Tremendous variation exists in how cities, states, countries, and other jurisdictional areas classify water use and consumption. Additionally, water conservation...     

Legal Implications of the Use of Big Data in the Transboundary Water Context

Water Resources Management - This article examines the potential legal implications of the adoption and implementation of transboundary water agreements and international water conventions as a...     

The Perception of Residential Water Tariff,Consumption, and Cost: Evidence of its Determinants Using Survey Data

In a survey of 1,500 households in Zaragoza (Spain), we found that respondents did not accurately know what the price of water was, and what the characteristics of the water tariff were, how much water they consumed, or how much it cost them; they thought they consumed less water than they actually did, and thought that their consumption level was normal; they believed they paid more than they really did, and considered the payments to be appropriate or expensive. Based on this sample of households, this paper analyses what determines the level of accuracy in the perception of consumers about their water tariff, their water consumption, and their total water cost, or, in other words, what determines the closeness between their perception and reality. The methodology applied is based on maximum likelihood estimates using various probit/logit models. The results obtained allow to identify the collectives to whom an information policy should be directed in order to approximate their perceptions closer to the real tariff, consumption and cost of water in households, and to point out the kind of information that should be transmitted in order to contribute to the preservation of water resources.