Weighted Linear Combination Research Articles

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Application of a GIS-Based Multi-Criteria Decision-Making Approach to the Siting of Ocean Thermal Energy Conversion Power Plants: A Case Study of the Xisha Sea Area, China

In order to achieve the goals of carbon neutrality and reduced carbon emissions, China is increasingly focusing on the development and utilization of renewable energy sources. Among these, ocean thermal energy conversion (OTEC) has the advantages of small periodic fluctuations and large potential reserves, making it an important research field. With the development of the “Maritime Silk Road”, the Xisha Islands in the South China Sea will see a growing demand for electricity, providing the potential for OTEC development in this region. Optimal site selection of OTEC power plants is a prerequisite for developing thermal energy provision, affecting both the construction costs and future benefits of the power plants. This study establishes a scientific evaluation model based on the decision-making frameworks of geographic information systems (GISs) and multi-criteria decision-making (MCDM) methods, specifically the analytic hierarchy process (AHP) for assigning weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to reclassify the factors, and weighted linear combination (WLC) to compute the suitability index. In addition to commonly considered factors such as temperature difference and marine usage status, this study innovatively incorporates geological conditions and maximum offshore distances of cold seawater based on cost control. The final evaluation identifies three suitable areas for OTEC development near the Xuande Atoll and the Yongle Atoll in the Xisha Sea Area, providing valuable insights for energy developers and policymakers.

Development and application of a WebGIS-based prediction system for multi-criteria decision analysis of porcine pasteurellosis

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

A three-step modification of the DRASTIC model using spatial multi criteria decision making methods to assess groundwater vulnerability

Groundwater vulnerability assessment, as a key tool, is essential for assisting decision-makers and experts in identifying highly vulnerable areas and devising efficient pollution reduction strategies. The widely utilized DRASTIC model for assessing groundwater vulnerability incorporates seven key geological and hydrological parameters. To address the limitations of the DRASTIC model, this study proposes a novel three-step modification by introducing an extra parameter, adjusting default weights, and utilizing alternative aggregation methods. These modifications aim to capture the unique characteristics of a specific region more accurately. The first step involves incorporating the Land Use (LU) parameter as the eighth parameter in the DRASTIC model, resulting in an updated 8-parameter model. The second step involves integrating the Analytic Hierarchy Process (AHP) to adjust parameter weights based on input from local experts. Lastly, while the Weighted Linear Combination (WLC) technique has conventionally been used for aggregating layers in the GIS environment, this study suggests employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Furthermore, to consider decision makers' risk attitudes, the Ordered Weighted Averaging (OWA) method is utilized.To assess its effectiveness, the modified model was applied to Khoy County in Iran. Then, the results were compared to the DRASTIC model using correlation analysis based on nitrate concentrations. The results indicated a correlation coefficient of 0.67 for the modified model compared to 0.47 for the DRASTIC model. This suggests that the proposed modified model is an enhanced and highly effective approach for evaluating groundwater vulnerability.

Landslide susceptibility assessment in Addi Arkay, Ethiopia using GIS, remote sensing, and AHP

Landslides account for the breakdown of natural topographies, impacting many mountainous areas and leading to loss of lives and damaged infrastructure. This research aims to generate a reliable landslide susceptibility zonation map employing geospatial and Analytical Hierarchy Processes (AHP) in Addi Arkay Woreda, North Gondar Zone, Amhara Regional State, northern Ethiopia. The present study uses remote sensing data, geographic information system (GIS) tools, AHP, and weighted linear combination (WLC) models to analyze multiple environmental variables, including slope, aspect, curvature, lithology, soil texture, topographic wetness index (TWI), and rainfall. As per the results, around 186.12 km2 (13.26%) of the total study area is under very high landslide susceptibility and 140.85 km2 (10.05%) under very low susceptibility. Using Google Earth images for inaccessible areas, 121 landslide inventories were identified through fieldwork. Of these inventories, 85 were used to train the model and 36 for testing. The performance of the AHP model was validated by the Receiver Operating Characteristics (ROC) curve (0.75), which indicates good predictive accuracy for identifying landslide-prone areas. These findings are essential to regional land use planning, hazard mitigation, and landslide prevention efforts. Additionally, this study contributes to the scientific understanding of landslide dynamics in the Northwestern highlands of Ethiopia and offers a methodological framework that can be applied to other regions with similar geological and climatic conditions.

Rainwater Harvesting Site Selection for Drought-Prone Areas in Somali and Borena Zones, Oromia Regional State, Ethiopia: A Geospatial and Multi-Criteria Decision Analysis

Rainwater collection systems play a crucial role in enhancing water availability in regions with low precipitation. This study focused on identifying potential rainwater harvesting (RWH) locations in drought-prone areas, specifically the Borena zone of Oromia and the regional states of Somali. This research leveraged geospatial techniques and a multi-criteria decision analysis (MCDA) to assess feasible RWH sites. The dataset comprises essential factors such as rainfall, drainage density, slope, soil texture type, and land use/land cover. These thematic layers serve as inputs for analysis, with each factor being weighted using the Analytical Hierarchy Process (AHP) method based on its significance. Reclassifying factors into subclasses facilitates suitability analysis. The weighted linear combination (WLC) technique is applied to identify and prioritize potential rainwater harvesting (PRWH) locations based on four suitability classes: highly suitable, moderately suitable, low suitability, and unsuitable. Our findings reveal that 1% of the study area, covering approximately 3288 km2, is highly suitable for RWH. Areas with moderate suitability constitute approximately 12% (37,498 km2), while regions with low suitability, representing the majority, encompass about 75% (242,170 km2). Additionally, 13% (41,000 km2) of the study area is deemed unsuitable for RWH. The proposed technique for identifying suitable RWH sites is adaptable to other low-precipitation regions. However, before implementing RWH structures, further research is imperative. This study proposed the exploration of socioeconomic variables in future research and urged for an in-depth examination of various aspects of environmental sustainability. Our research paves the way for adapting rainwater harvesting systems to align with community needs and life cycles while also exploring the socio-economic and environmental dimensions of sustainability for future study. The insights offer promising solutions to address the urgent issues associated with water scarcity. This should include comprehensive site depictions, an exploration of social and economic activities, and the meticulous preparation of a cost-benefit analysis.

An improved arithmetic optimization algorithm with multi-strategy for adaptive multi-spectral image fusion

Panchromatic and multi-spectral image fusion, called panchromatic sharpening, is the process of combining the spatial and spectral information of the source image into the fused image to give the image a higher spatial and spectral resolution. In order to improve the spatial resolution and spectral information quality of the image, an adaptive multi-spectral image fusion method based on an improved arithmetic optimization algorithm is proposed. This paper proposed improved arithmetic optimization algorithm, which uses dynamic stochastic search technique and oppositional learning operator, to perform local search and behavioral complementation of population individuals, and to improve the ability of population individuals to jump out of the local optimum. The method combines adaptive methods to calculate the weights of linear combinations of panchromatic and multi-spectral gradients to improve the quality of fused images. This study not only improves the quality and effect of image fusion, but also focuses on optimizing the operation efficiency of the algorithm to have real-time and high efficiency. Experimental results show that the proposed method exhibits strong performance on different datasets, improves the spatial resolution and spectral information quality of the fused images, and has good adaptability and robustness. The source code is available at: https://github.com/starboot/IAOA-For-Image-Fusion.

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Allocation of reservoirs sites for runoff management towards sustainable water resources: Case study of Harirud River Basin, Afghanistan

The intensification of drought conditions in arid and semi-arid regions coupled with the growing population increased water demands (domestic, and irrigation). It signifies the importance of addressing the pressing need for sustainable water resource management. Seventeen criteria spanning topographical, hydrological, geological, environmental, and socio-economic categories, were used for dam site suitability analysis for Harirud River Basin, Afghanistan. Weighted Linear Combination (WLC) model and Geographic Information System (GIS) were employed to perform multi-criteria analysis (MCA) method. The results revealed that the final suitability map exposed 12.31 % highly suitable, 21.0 % moderately suitable, and 66.68 % non-suitable sites, totaling 125.8 km, 214.6 km, and 681.4 km, respectively. Segmentation accuracy assessment was utilized to evaluate the existing dam’s accuracies. The study revealed that accuracies of 84.65 % for the Salma Dam and 88.08 % for the Pashdan Dam were achieved, respectively. Six new dams, along with the existing two, formed the series of eight dams. The dam series were optimized for the total annual runoff of (1,600,000,000 m3), and the final dam heights were determined as follows: 107.5 m, 42 m, 100 m, 23.9 m, 30 m, 18 m, 23.44 m, and 22.65 m for dams (1 to 8), respectively. The study underscores the methodology's superiority in site suitability analysis and runoff optimization, while recommending future focus on observational data and higher-resolution digital elevation model (DEM) files for enhanced precision, environmental impact assessment, climate change considerations, social and economic implications, improved data resolution, and transboundary cooperation.

Offshore wind power plant site selection in the Baltic Sea

The selection of suitable sites for offshore wind power plants is an important marine spatial planning problem in the Baltic Sea. The application of multiple and different decision support tools (DSTs) can facilitate site selection. This study employs a geographical information system (GIS) integrated with the levelized cost of energy (LCOE) model, and GIS with multi-criteria decision analysis (MCDA) using an analytical hierarch process (AHP), fuzzy logic, weighted linear combination (WLC), and the technique for order of preference by similarity to ideal solution (TOPSIS) as an extension of MCDA. The core novelty of the study is the use of several concurrent state-of-the art techniques to quantify the detailed spatial pattern of suitability of different sea areas for offshore wind power plant installations for the entire Baltic Sea at an unprecedented, for such a large sea area, resolution. Important parameters were identified and data from the Global Wind Atlas, HELCOM, and EMODnet portals were used. The most appropriate areas exhibit LCOE values between 42 and 58 €/MWh, MCDA values in the range of 0.78–0.92, and capacity factor values from 0.52 to 0.55. The areas identified as the least appropriate were characterized by LCOE values ranging from 107 to 210 €/MWh, MCDA values between 0.34 and 0.59, and capacity factor values within the range of 0.30–0.43. The outcomes using both methodologies were similar. The decision preferred are mostly locations close to shore in shallow water and with a high capacity factor. The largest suitable areas are in the south-west Baltic Sea whereas some suitable locations are identified further to the north and east.

Linking wilderness mapping and ecosystem services: Identifying integrated wilderness and ecological indicators to quantify ecosystem services of wilderness

Wilderness has proven to provide a wide range of ecosystem services (ES). Wilderness have been shown to play an important role in preserving biodiversity and providing ES, which is indispensable to both nature and people. It is essential to integrate ES quantification into wilderness conservation and management. This study presents a method that combines the wilderness mapping and ecological indicators to measure ES of wilderness. The method was applied in 155,800 km2 of Shandong province in China, across 23 types of land use. First, relative wilderness value (RWV) and ecosystem service capability (ESC) were quantified according to Weighted Linear Combination (WLC). Then, spatial autocorrelation between RWV and ESC were tested and analyzed using GeoDa software. Finally, the Redundancy analysis was used to investigate the primary drivers of RWV and ESC in different landscape types. The findings showed that, there were 27,263 km2 of wilderness in Shandong province, of which 6,294 km2 were high-quality wilderness. The region with the highest RWV was the Yellow River Delta (RWV = 0.82), and the area with the highest ESC was Laoshan Mountain (ESC = 0.76). Spatial autocorrelation analysis showed a positive correlation between RWV and ESC (Pearson’s r = 0.637), both of which had significant spatial relevance (Moran’s I = 0.589, P < 0.005). The RWV and ESC high-high clusters were found to cover 5,223 km2 of high-quality wilderness areas. Regression analysis (R2 = 0.744) showed that RWV can quantify ESC of urban area reliably. Population density (Explaining 33.9 %-49.3 %) was found to be a key indicator of RWV and ESC changes through the Redundancy analysis. In addition, we discussed key drivers of RWV and ESC of high-quality wilderness according to different landscape types. This approach can be used and adopted to support planning decisions dealing with wilderness conservation and management.

Case Study on Spatial Mismatch between Multivariate and Student-Teacher Rate in U.S. Public School Districts

An important aspect of educational equity is the balance between students and teachers in the general education system. To address the need for a sustainable, balanced, and reliable supply of high-quality STEM teachers for public school districts, this research aims to illustrate the spatial dynamics of student-teacher balance in the impact of teacher salary, school poverty, transportation, and environmental factors from 2015 to 2020, Data were collected to create a multivariate evaluation via Analytic Hierarchical Process (AHP), Compromise Programming (CP), weight linear combination and Spatial Mismatch Index Model (SMI) to reveal the non-synergistic coupling relationship between multivariate and student-teacher rate in school districts, counties, and state multiple levels. The results suggest that compared to 10% of the spatial mismatch index at the state level, the proportion of mismatched areas at the school district and county levels was the same at 1%. NV, IN, VT, MA, and FL were mismatched at the state level but had good matches at the county and school district levels. Other unpredictable factors related to teacher shortages, such as workload, school rankings, and teacher vacancies, should be considered for further study in future research plans. This research provides valuable insights for policy interventions to improve the treatment of teachers in public school districts and promote educational equity.

Geospatial technology for selecting suitable sites for solid waste disposal: a case study of Shone town, central Ethiopia

ABSTRACT The main objective of this study is to choose appropriate solid waste disposal sites based on environmentally friendly, cost-effective, and socially responsible factors using geospatial technology. To achieve the study’s objectives, factor maps such as slope, settlement, road, stream, land use/land cover, geology, borehole, protected area, and soil maps were used and prepared using GIS. The most appropriate solid waste disposal sites were identified and categorized as highly suitable, suitable, less suitable, or unsuitable using a weighted linear combination (WLC) analysis. Through ArcMap overlay analysis, the final suitability map was created. The AHP pairwise comparison module helped establish the weight of factor parameters. The findings reveal that out of the total area, 51.32 hectares are highly suitable, 54.86 hectares are suitable, 34.99 hectares are less suitable, and 3171.22 hectares are unsuitable for solid waste disposal. The use of GIS and remote sensing data has allowed for a comprehensive evaluation of multiple factors and their weighted importance, resulting in a final suitability map that can guide decision-makers in selecting appropriate sites for solid waste disposal. In conclusion, the study provides a practical solution to the problem of solid waste disposal by using geospatial technology.

GIS-based land suitability analysis for the optimal location of integrated multi-trophic aquaponic systems

Aquaponics has witnessed global proliferation and a notable enhancement in sustainability in recent years. Consequently, it assumes paramount importance to delineate optimal locations for its implementation, in fact, the success of an aquaponic facility also depends on its geographical placement, necessitating consideration of many variables encompassing natural resources, socioeconomic factors, infrastructural availability and environmental constraints, whether natural or artificial. This paper focuses on the definition and test in the Emilia-Romagna region (Italy) of a GIS-based multi-criteria land suitability assessment model aimed at allowing the diffusion and environmental integration of innovative integrated multi-trophic aquaponic systems. The process has been implemented with a Weighted Linear Combination (WLC) model, where decisions and criteria were selected via a participatory mechanism involving experts in various fields. The region has been subdivided into 50 × 50 m grid cells, with each grid cell being associated with a value ranging from 0 to 8. In this context, a rating of 0 means unsuitability, while a rating of 1 denotes minimal suitability, and the highest rating of 8 designates maximal suitability. Notably, a substantial portion of the surveyed territory has been found to be completely unsuitable for the establishment of aquaponic facilities. More than 86.4% of the remaining suitable areas were rated 6, 7, or 8, affirming the overall favourability of the Emilia-Romagna region for aquaponic installations. Finally, the veracity and robustness of the results have been tested through a one-at-a-time sensitivity analysis, that has proven the appropriateness of the proposed model.

Land use suitability analysis using an approach combining GIS, regionalization, and PROMETHEE II

ABSTRACT Land-use Suitability Analysis, which aims to identify the suitability of a given area for a particular use by using a certain number of criteria, is a domain that has received a lot of attention from researchers during the last 20 years and most of the works that have been proposed rely on the use of multi-criteria methods which are based on a complete aggregation of alternatives such as Weighted Linear Combination (WLC). The outranking methods, which are multi-criteria methods that rely on a partial aggregation of alternatives, have rarely been used. In the context of Land-use Suitability Analysis, outranking methods quickly reach their computational limits because they require performing comparisons between each pair of pixels. To reduce the initial number of alternatives, we propose to proceed to the regionalization of the study area by grouping neighbouring pixels that have similar properties in homogeneous zones. We then use PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations), an outranking method whose objective is to rank a set of alternatives from the best to the worst. A study for the choice of a landfill site was carried out using our approach and the obtained results were compared with those of WLC.

The Identification of Landslide Risk-Prone Areas in the Imbulpe Divisional Secretariat in Sri Lanka: A GIS-Based Multi-Criteria Decision Analysis

Landslides can be identified as a prominent natural hazard that causes significant loss of life and property damage in many parts of the world and Sri Lanka. Planning experts, local governments, and decision-makers can benefit greatly from the identification of landslides and the creation of landslide susceptibility maps. Accurate landslide susceptibility maps are essential to minimize the loss of life and property. The landslide risk zones in any area can be plotted using GIS-based MCDA techniques that integrate the Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) functions in ArcMap. This study attempted to develop a multi-criteria decision analysis for the appraisal of landslide risk zones in the Imbulpe DSD in Rathnapura District. To analyze the danger of landslides, MCDA was used in conjunction with GIS as a spatial decision-making tool. Slope, drainage pattern, soil type, geology, land use, and rainfall pattern were the six factors used in the study. Landslide risk maps were produced based on weighted techniques such as Analytic Hierarchy Process and Weighted Linear Combination. The slope area significantly affects the likelihood of a landslide. Results revealed that landslides were spreading in a manner that extended toward the eastern section of the study area. Because there are no good locations to build houses in the study area, a huge number of individuals tend to live in unprotected areas. The majority of the landslides in the area occurred during May, October, and November because those months received heavy rainfall. As remedial actions, it can be proposed to displace people living under landslide risk in the northeast and northwest slope areas to suitable protected areas.

Modeling landslides hazard along Amman–Jerash–Irbid Highway, Jordan by integrating open street map (OSM) and weighted linear combination (WLC) techniques

Modeling landslides hazard along Amman–Jerash–Irbid Highway, Jordan by integrating open street map (OSM) and weighted linear combination (WLC) techniques

A Study on the Impact of Higher Education Expansion on Human Capital Accumulation and Economic Development - An Empirical Analysis Based on Structural Equation Model

Abstract Based on structural equation modeling, this paper investigates the impact of expanding higher education levels on human capital accumulation and economic development. The relationship between latent and latent variables is used to estimate the maximum likelihood estimates of scale expansion by calculating unstabilized and stabilized weights. A weighted linear combination of weights in the weights pool is used to construct the SL estimator. Based on the structural equation model, the prediction model of the impact of higher education scale expansion on human capital and economic development is constructed, and four impact hypotheses are proposed. The results show that the rate of return to higher education for expanding the higher education scale is 4.36%. The rate of human capital accumulation is 102.36%, while the level of economic development is 63.89%. It indicates that the positive effect of expanding higher education levels on human capital accumulation and economic development is significant.

Identification of priority areas for water conservation in the Alto Iguaçu basin, Brazil.

Geoprocessing tools are great allies in the management of water resources, permitting the evaluation of several factors integrated into a multicriteria analysis. This improves the interpretation of the behavior of water and soil in hydrographic basins, allowing the delimitation of priority areas for actions. The objective of this study was to prepare a map of priority areas for water conservation in the Alto Iguaçu basin through a multicriteria analysis, aiming to assist in the management of water resources in the region. The priority map for water conservation was prepared using the weighted linear combination (WLC) method, considering the weight added to the factors that were obtained by the analytic hierarchy process (AHP) method. The method successfully identified priority areas for water conservation, where 40.7% of the Alto Iguaçu watershed was considered High priority and Very High priority, a number consistent with regional characteristics. The multicriteria analysis proved to be a flexible and easy to implement methodology, which can help managers when considering the trade-offs among several factors. The implementation of water conservation actions in areas that have been classified as being of High and Very High priority can benefit the regional ecosystem by controlling erosion, regulating the flow of water, and thus conserving the quantity and quality of water in the basin and its downstream region, benefiting the population and regional economics. Integr Environ Assess Manag 2024;20:1087-1098. © 2023 SETAC.

Identifying groundwater prospect zones through earth observation techniques in Bilate watershed, Rift Valley Lakes Basin, Ethiopia

ABSTRACT Groundwater is one of the most valuable natural resources gifted to mankind, especially in surface water-scarce locations. Most parts of Ethiopia dependent on the amenity for common needs, irrigation, etc. The lack of a scientific approach to its exploitation and management makes the resource underutilized. Therefore, Remote sensing (RS) and Geographic information systems (GIS) were utilized to decipher potential groundwater zones (PGWZ) existing in the Bilate watershed of the nation to economize, speed up and technicalize the system. The ground truth of the results obtained was verified to confirm the reliability of the techniques used. Through RS, optical and microwave data were obtained, while GIS was helpful in collecting information on water points mapping and 2D resistivity survey in locating the aquifers. In particular, resistivity studies revealed both pyroclastic and rhyolite intrusions that act as water barriers and the basaltic and ignimbrite rocks that favour aquifer affluence. During the process of identification and quantification of PGWZ in the watershed, the analytical hierarchy process (AHP), weighted linear combination (WLC), and digital elevation model (DEM) were employed in thematic layer formation, reclassification, overlay analysis and map production. As a result, five PGWZ having very high (19.8%), high (35.3%), moderate (22.6%), low (19.3%), and very low (2.9%) prospects were identified. Similarly, over 75% of the area was moderate to high category in PGWZ. The accuracy of the qualitative groundwater inventory was 92.64 per cent. Thus, combining high-resolution optical/microwave data and GPS information with the AHP model has ascertained the accuracy of the PGWZ identified. So ultimately, the present work lays the foundation for the use of advanced spatial techniques in finding natural resources useful for the welfare of the nation and its populace.