Robust programming for basin-level water allocation with uncertain water availability and policy-driven scenario analysis

Abstract

Uncertainties from hydrological and meteorological environments constantly pose disturbances to water sustainability. Programming under such uncertainties aims at finding solutions to this risky condition. From the sight of uncertain water availability, this paper builds a water life cycle model to reduce the risks of inappropriate estimations of water availability within a river basin and incorporates the results in robust programming. Then, a policy-driven scenario analysis is conducted to provide managerial implications in terms of ongoing water-saving policies. With Min–Tuo river basin as the case, we finally reach the conclusions that: (1) Equity is a necessity when considering the water allocation in a river basin, which enables a more sustainable mode of local water use. (2) Local citizens’ willingness to follow the policies is a key to relieve the water pressure, while the progress of water-saving techniques could add to its effectiveness.

Desempeño Gubernamental Y Cambio Climático en Políticas De Gestión Hídrica Del Lago Titicaca

“…Es crucial considerar los efectos del cambio climático en los sistemas hídricos para garantizar la sostenibilidad y provisión de agua potable segura y sostenible con una gestión del agua eficiente (Sperotto et al, 2019;Vaseashta et al, 2022). La incertidumbre hidrometeorológica impacta la sostenibilidad del agua, prescindiendo de análisis de escenarios para orientar políticas de conservación y evaluar la sostenibilidad en la distribución de agua, facilitando decisiones y visualización de resultados sostenibles (Claassen, 2022;Haro-Monteagudo et al, 2020;Yao et al, 2022).…”

Guest editorial on “data-driven operations management”

“…To accurately predict the survival status of cancer patients, the authors in the third paper, "A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction" [11], developed a two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction. Specifically, a priori knowledge-and stability-based feature selection (PKSFS) method was introduced to obtain the optimal feature subsets from the high-dimensional cancer datasets to guide the subsequent model construction, and then a novel two-stage heterogeneous stacked ensemble learning model (BQAXR) was devised to generate five high-quality heterogeneous learners, and integrate them in two stages through the stacked generalization strategy based on optimal feature subsets.…”

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Data-driven operations managementWe can roughly divide the accepted 15 papers into four groups according to their topics: data-driven supply chain management [1-4], data-driven process scheduling [5-8], data-driven healthcare operations management [9][10][11], and other data-driven operations management problems [12][13][14][15]. In the following, we formally introduce related works in detail.

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A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

“…Gong et al [17] proposed an inexact programming model for optimizing irrigation water resources based on crop water requirements in consideration of effective precipitation and uncertainty. Yao et al [18] proposed robust programming for basin-level water allocation with uncertain water availability and a policy-driven scenario analysis. Xu et al [19], using a combination of an interpretative structural model (ISM) and an analytical network process (ANP), developed a hierarchical structure model, that is, composed of direct factors, indirect factors, and basic factors.…”