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[Display omitted] • Groundwater vulnerability zonation investigation is conducted for GHM Greater Bay Area. • Three different perspectives are comprehensively considered in this study. • Groundwater risk, function value and contamination status are superposed using the AHP method. • Quantification and visualization of groundwater contamination prevention regionalization. • The AHP-DRASTICH model is an effective method to evaluate the groundwater risk in shallow aquifers. The groundwater contamination assessment and prevention regionalization are an important part of groundwater investigation, which is of great significance to groundwater environmental management and contamination control. Although the Guangdong-Hong Kong-Macao Greater Bay Area (GHM Greater Bay Area) has faced significant groundwater contamination since 2020, there's been no investigation into its vulnerability zonation. In this paper, groundwater in a typical area of south China was taken as the research object. We analyzed groundwater contamination risk by considering both the groundwater vulnerability and the load from contamination sources. Using the analytic hierarchy process (AHP) method, we superposed the groundwater risk, function value and contamination status, and realized the quantification and visualization of groundwater contamination prevention regionalization. Based on the DRASTIC model, the zones of low, moderate, high, and very high vulnerability are respectively 14.2 %, 24.28 %, 37.67 %, and 20.45 % of the study area. Results show that the groundwater vulnerability grade in the area is III-IV, which means it is easy to be polluted. The functional value of groundwater in the south-central part of the region is relatively high. Most of the areas with high prevention and control levels are located in plain areas and the Piedmont plain area with an area of 460 km2. The secondary control levels (key protection area) are mainly located in the middle, southwest, and northeast of the plain, with an area of 6944 square kilometers. Consequently, the ideas and contamination prevention zone maps might provide a scientific foundation for long-term planning and management and groundwater conservation in the Guangdong-Hong Kong-Macao Greater Bay Area and peripheral zones. [ABSTRACT FROM AUTHOR]

• Preliminary assessment of grey infrastructure sites at flood hotspots maximizes its advantages. • Green-grey infrastructures jointly improve their benefits over separate applications. • Optimal design of green-grey infrastructures is more cost-effective for higher storms vs lower. • Retrofitting green-grey infrastructure depends on available resources and target design goals. Green-grey infrastructures implementation for addressing urban flood damages and urban drainage system' (UDS) resilience (respectively) needs comprehensive evaluation under various planning and design influencing factors. In this paper, a multi-objective decision-making framework is developed for the optimization of green-grey infrastructures to understand the trade-off between benefits and retrofit costs under various design configurations, rainfall intensities, and system performance indicators. In the developed framework, the grey infrastructure locations are optimally determined by using Analytic Hierarchy Process (AHP) method with three defined indicators (e.g., flood volume and flood duration of flooding nodes, sensitivity of flooding nodes) and maximum percentage of each type of green infrastructure (GI) in each sub-catchment based on land-use analysis as a preliminary step of the multi-objective optimization process, followed by decision-making procedure. The application results reveal that optimal designs of grey infrastructure are more cost-effective and may have a significant influence on the reduction of flood damages and outlet peak flow compared to GI from their individual evaluations, while GI improves UDS' functionality performance indicators (e.g., resilience, reliability, and sustainability) better than grey infrastructure. However, a combined implementation of both infrastructures boosts their advantages because of the synchronization effect compared to individual scenarios, providing the most cost-effective optimal UDS designs. Moreover, the cost-effectiveness of coupled green-grey infrastructures designs could be enhanced from lower to higher design storms because of higher rate of utilization of stormwater infiltration and storage capacities of both infrastructures. The framework presented in this study can be conveniently generalized to other case-studies for optimal implementation of green-grey infrastructures as long as including their local economic parameters, climatic patterns, urban development plans and green-grey infrastructures design guidelines. [ABSTRACT FROM AUTHOR]

• Drywells are effective facilities in recharging stressed urban shallow aquifers. • Proper sitting is of utmost important for their performance and pollution mitigation. • Hybrid MCDA model for delineating auspicious zones for drywells implementation. • Comparison between obtained criteria weights and those adopted in similar studies. • The model is expected to offer a comprehensive framework to states lacking drywells guidelines. Artificial recharge in urban aquifers is of vital importance due to the increase in urbanization resulting in a reduction in natural recharge along with groundwater overdraft in many regions across the globe. Despite existing several managed aquifer recharge (MAR) technologies, land availability for installation and limited municipal budget restrict their usage, especially in urbanized areas. Hence, drywells were introduced as a vadose zone recharge (VZR) facilitating the recharge of stressed aquifers with low land impact and financial resources. Also, identifying suitable sites for drywell implementation is complex due to the number of driving factors involved. In this paper, the integrated fuzzy analytical hierarchy process (FAHP) and technique for order of preference by similarity to ideal solution (TOPSIS) approach was proposed to delineate a site suitability map for artificial groundwater recharge using drywells in the city of Kayseri, Turkey. Nine decision criteria were selected and the weights of each (sub-) criterion were determined through the FAHP. The results indicated that hydraulic conductivity, depth to groundwater, soil texture, and land use/land cover were the most determining criteria with 33.9%, 17.8%, 11.2%, and 10.1% of weights, respectively. Also, the stability and robustness of the criteria weights attained were validated through sensitivity analysis within the FAHP framework. Finally, the study area was divided into 54 sub-regions based on the road-network and the TOPSIS was applied to prioritize the sub-regions to help identify promising areas for the vadose zone wells construction. Here, a similar approach can be adopted by municipalities lacking guidelines and standards for sitting drywells. [ABSTRACT FROM AUTHOR]

[Display omitted] • Presents definitions and the study regions involving groundwater potential in the world. • Presents sources and types of data used to map groundwater potential. • Highlight the research models and analyze its characteristics. • Presents methods to determine the accuracy of the models. This paper aims to review parameters, model techniques, validation methods in groundwater potential field. According to statistics, there are three major model groups used to establish groundwater potential maps. The first model group is a statistic group, including multi-criteria decision making/analytic hierarchy process, frequency ratio, evidence belief function, and weights of evidence. The second model group includes machine learning models, such as random forest, logistic regression, boosted regression tree, and support vector machine. The final group is the hybrid/ensemble models. In groundwater potential mapping studies, 41 thematic layers affect the potential of groundwater. However, hydrological researchers have frequently used eight factors in groundwater potential studies: geology, slope, land use, soil type, drainage density, lineament density, altitude, rainfall. Most previous studies on groundwater potential have used a combination of geographic information system, remote sensing, and machine learning techniques to design the groundwater potential in regions of interest. Data sources are commonly applied to groundwater potential mapping, including satellite, borehole, and geophysical data. The accuracy of groundwater potential maps produced by common machine learning models ranges from 50.0% to 90.1%, while that produced by common statistical models ranges between 59.0% and 90.3%. Interestingly, hybrid/ensemble models' accuracy interval ranges from 71.0% to 92.0%. Therefore, the review suggests that statistical algorithms and machine learning techniques should be combined, and thematic layers should be increasingly used in mapping groundwater potential maps to achieve high efficiency. [ABSTRACT FROM AUTHOR]

• The primary requirements of multi-objective have been recognized and guaranteed. • MOSRR has been proposed to alleviate the fierce competition among multiple objectives. • MOSRR can achieve the equilibrium of primary and secondary requirements. • MOSRR improve multi-objective at the expense of a reduction in power generation. • MOSRR can combat runoff reduction by minimizing the damage of primary requirements. Multi-objective requirements including water supply, hydropower generation, water–sediment regulation (WSR), and inner-river ecosystem maintenance should be fully valued in the operation of reservoirs located on a sediment-laden river. In order to balance the satisfaction of multiple objectives with intense competition and guarantee the primary requirements preferentially, this paper presents a novel framework that incorporates the recognition and classification of primary and secondary requirements of multiple operation targets, a set of principles for multi-objective synergetic regulation of reservoirs (MOSRR) following the theory of synergetics, and the optimization model construction of MOSRR considering the maximization synergetic degree of multi-objective. The Analytic Hierarchy Process and Particle Swarm Optimization algorithm are employed to determine the weight and solve the MOSRR model, respectively. MOSRR is applied to a system of cascade reservoirs along the Upper Yellow River in China considering climatic and hydrologic variability. The results from MOSRR are compared to those from a traditional multi-objective optimal operation model. It is found that the guarantee of multi-objective requirements from MOSRR have been improved visibly compare with the traditional model at the expense of a slight reduction in the hydropower generation (especially the secondary requirement of the energy output in the dry season). Also, MOSRR can mitigate the effects of runoff reduction through minimizing the damage of primary requirements, which is more appropriate for multi-objective reservoir operation with limited water in sediment-laden river. The proposed MOSRR framework can be applied to other large-scale reservoir systems with stiff competition in water resources. [ABSTRACT FROM AUTHOR]

• A viable method is proposed for spatial design of water quality monitoring network. • The proposed method was applied in surface water and a road construction project. • The integrated form of transinformation entropy and value of information was used. • The application of NSGA-II and III based optimization model were assessed. • The TOPSIS, PROMETHEE, and AHP models were utilized for decision-making. The environmental impacts of road construction on the aquatic environment necessitate the monitoring of receiving water quality. The main contribution of the paper is developing a feasible methodology for spatial optimization of the water quality monitoring network (WQMN) in surface water during road construction using the field data. First, using the Canadian Council of Ministers of the Environment (CCME) method, the water quality index (WQI) was computed in each potential monitoring station during construction. Then, the integrated form of the information-theoretic techniques consists of the transinformation entropy (TE), and the value of information (VOI) were calculated for the potential stations. To achieve the optimal WQMNs, the Non-dominated Sorting Genetic Algorithm II and III (NSGA-II, and III) based multi-objective optimization models were developed considering three objective functions, including i) minimizing the number of stations, ii) maximizing the VOI in the selected network, and iii) minimizing redundant information for the selected nodes. Finally, three multi-criteria decision-making models, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), and Analytical Hierarchy Process (AHP) were utilized for choosing the best alternative among Pareto optimal solutions considering various weighing scenarios assigned to criteria. The applicability of the presented methodology was assessed in a 22 km long road construction site in southern Norway. The results deliver significant knowledge for decision-makers on establishing a robust WQMN in surface water during road construction projects. [ABSTRACT FROM AUTHOR]