Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)

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 challenge in such areas. Against this background, this study proceeds to utilize and compare five decision tree-based data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

/pdf/decision-tree-based-data-mining-and-rule-induction-for-59ys0x0f73.pdf

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

Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model

The economic impacts of water supply restrictions due to climate and policy change: A transboundary river basin supply-side input-output analysis

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

/pdf/applications-of-bayesian-networks-as-decision-support-tools-2nm706o5et.pdf

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Sustainable utilization of water resources requires preventive measures that must be taken to promote optimal use of water resources together with consideration of stakeholder interests and the economic value of water. The main objective of this study is to present an integrated hydro-economic model for allocating agricultural water based on its economic value. The study region covered six irrigation networks downstream of the Zayandeh
 Rood Dam in Iran. In fact, this study addresses questions of how to allocate scarce water to different consumers, in order to achieve the highest efficiency and economic benefits. To gain this goal, the existing agricultural activities in each irrigation network were simulated by applying the Positive Mathematical Programming (PMP) economic model and then by coupling the economic model with a water allocation planning model of the basin (MODSIM), the hydro-economic framework was generated. These tools helped to allocate water based on its economic value, maximize net profit by determining the optimal cultivating area and analyze the effects of various allocation scenarios on employment, economic productivity, and reliability indicators. Moreover, Bayesian Networks and Random Forest algorithms were developed as an automated substitute to simplify the process and reduce computational complexity. The results showed that the Nekoabad Network enjoys top priority followed by the Barkhar, Mahyar, Sonati, Abshar, and Rodasht Networks. After implementing the Bayesian Network, the four criteria of MAE, MAPE, R2, and the Nash-Sutcliffe index for the irrigation networks were 9 MCM, 24%, 0.738, and 0.644 respectively, which indicated the model has good accuracy. Random Forest method was also employed as a novel technique in automated allocation, and the values obtained for the four mentioned criteria were 7 MCM, 15%, 0.861, and 0.80, which showed it is more accurate. The results indicated the capability of the presented hydro-economic model as well as the intelligent models substituting it in allocating agricultural water.

Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches

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Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches