GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region

The site selection process for a building entails evaluating a variety of factors with varying degrees of importance or percentage influence. In order to ensure that critical site selection factors are not overlooked, a methodology for calculating a building’s safe site selection must be developed. The study identified three broad aspects widely considered in site selection, namely environmental, physical, and socioeconomic criteria. To assess the safest site selection of residential building construction for sustainable urban growth, we used GIS-based multi-criteria decision-making approach that combined Fuzzy-AHP and weighted linear combination (WLC) aggregation method used to calculate the SSPZ. The final safe site suitability map was generated by aggregating all aspects such as geophysical, socio-economic and Geo-environmental thematic layers and their associated Fuzzy-AHP weights using the weighted linear combination method. The sites potential index’s mean value of 0.513 with standard deviation of 0.340, minimum and maximum GeoPhySSSI are 0.0 and 0.91, respectively, SSS index is classified into zones by histogram profile using natural breaks (jenks)” Subsequently, safe sites identified and divided into six classes namely no construction, very low suitable site low suitable site, moderate suitable site, high suitable site, and very high suitable site.“ According to the statistical analysis, 3.64% and 32.12% of the total area were under very high and high SSSZ, while 26.40% and 6.22% accounted to the moderate and low suitable potential, respectively” Our findings suggest that integrating the fuzzy collection with AHP is highly desirable in terms of alternative and decision-making effectiveness. The study reveals that the areas of high and moderate suitability are located near existing habitant area, major roads, and educational and health services; they are not located in restricted/protected areas or are vulnerable to natural hazards. The findings indicate that unsuitable and less-suitable land uses such as vegetation, protected areas, and agriculture lands cover nearly one-third area of Abha-Khamis Mushyet regions, implying that using Fuzzy-AHP and GIS techniques will significantly aid in the conservation of the environment. This would significantly mitigate adverse effects on the ecosystem and climate.

Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.

Application of Suitability Modeling in Establishing a New Bicycle–Pedestrian Path: The Case of the Abandoned Kanfanar–Rovinj Railway in Istria

This paper presents research on the conversion of the abandoned Kanfanar–Rovinj railway into a bicycle–pedestrian path, with the aim of including it in the existing network of bicycle–pedestrian paths in the Istria County in Croatia. This would enable better connection of the repurposed railway corridor with the surrounding natural and cultural values and better use of the spatial potential, which would create more favorable conditions for the revitalization of the wider rural environment. In order to identify the existing potential of the area, as well as the impact of the proposed intervention on critical areas, a method of multicriteria analysis was used. The result of the analysis was a model of the suitability of the space, i.e., five value categories that make the space more or less suitable for accommodating new pedestrian and bicycle paths. In order to emphasize the importance of individual spatial contents in the modeling process, the method of weighted linear combination was used. Finally, the nature of the project and its potential impact on the environment have conditioned the selection of the appropriate aspect of the model and of the space’s suitability, which is further used for locating the new pedestrian and bicycle paths.

Investigation of Groundwater Potential Using Remote Sensing and Geographical Information System (GIS) Techniques in Fakai Local Government of Kebbi State, Nigeria

Groundwater is one of the most precious natural resource which supports human health, economic development and ecological diversity. Remote sensing and Geographical Information System (GIS) Techniques have been effectively used for the investigation of the potentiality of groundwater resource in Fakai local government area. The dataset for this research work are Landsat 8 Operational land imager (OLI), ASTER DEM, Topographical map and Geological map from which the essential criteria were obtained. The study used Weighted Linear Combination approach which involves mathematical weighing and ranking of the criteria. Multi-criteria evaluation was carried out on all the criteria using the Weighted Linear Combination approach in ArcGIS 10.4. Spatial analysis was carried out on the derived result using the Suitability Index (SI) value created from pairwise comparison analysis. The suitability map for groundwater recharge in the study area was hence produced using the suitability index. The result shows four classes for the study area. The classes include highly suitable, moderately suitable, less suitable and least suitable. Thus, the area most suitable for groundwater are found most towards the northern part, around the center and some regions in the northern part of the study area this serves as an indicator that most of the study area has good potential for groundwater recharge.

Weighted Linear Combination Procedures with GIS and Remote Sensing in Flood Vulnerability Analysis of Abeokuta Metropolis in Nigeria

The study offered the opportunity for an evaluation of the role of Remote Sensing and Geospatial techniques in flood disaster risk management and development of spatial decision support system for flood risk assessment and management in Abeokuta metropolis. Datasets used includes cloud free high resolution satellite images and Shuttle Radar Topographic Mission (SRTM) data downloaded from earth explorer site. Soil data used was obtained from Food and Agriculture Organization (FAO’s) Harmonised World Soil Database, while rainfall data was obtained from the Climate Hazards Group InfraRed Precipitation Station. Maps of flood enhancing factors namely: soil types, rainfall intensity, drainage density and topography were created in Geographic Information Systems using same scale of 1: 50,000 and Geographic coordinate system (WGS 1984). All maps were produced in raster format with the same cell grid cell size of 0.0028 mm. They were then subjected to weighting by ranking and Multi-Criteria Analysis (MCA) using the Weighted Linear Combination. The study identified topography and land use as key factors contributing to flooding within Abeokuta metropolis. Obstruction of natural drainage channels by buildings aggravates disasters from flash flood events.

Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences

This study was aimed at optimizing the weighted linear combination method (WLC) for agricultural land suitability evaluation (ALSE) through indicator selection, weight determination, and classification of overall suitability scores in Handan, China. Handan is a representative research area with distinct agricultural advantages and regional differences in land use, where the expansion of construction land has led to a rapid decrease of agricultural land in recent years. Natural factors (topography, climate, soil conditions, and vegetation cover) and socioeconomic factors (land use and spatial accessibility) were selected to establish a more comprehensive evaluation system. The index weight was calculated by the mutual information between index suitability and current land use. The consistency index was used to identify the boundary value dividing the overall suitability score into a suitable category and unsuitable category in each sub-region. The results demonstrated that the optimized WLC-ALSE model outperformed the comparison models using conventional methods in terms of the consistency between the evaluation results and current land use. Owing to the increasing limitations of topography, soil conditions, spatial accessibility, and land use, the proportions of suitable land in Zone 1, Zone 2, and Zone 3 were 77.4%, 67.5%, and 30.9%, respectively. The agricultural land unsuitable for agriculture (14.5%) was less than non-agricultural land suitable for agriculture (7.4%), indicating that agricultural land had low growth potential in Handan. Finally, specific recommendations were made to improve agricultural land suitability, alleviate land use conflicts, and further optimize the model. The results can provide effective guidance for WLC-ALSE and land use decision-making for sustainable agriculture.

Geodiversity Assessment with Crowdsourced Data and Spatial Multicriteria Analysis

This paper presents an approach to geodiversity assessment based on spatial multicriteria analysis. Instead of relying solely on weighted linear combination (WLC) for aggregating factor ratings and weights to compute a synthetic measure of geodiversity, the approach employs WLC in concert with its local version called L-WLC to provide a more comprehensive assessment approach. As part of the approach, the assessment input data comprised of geodiversity factor ratings and weights were obtained through crowdsourcing. A geoinformation crowdsourcing tool called the geo-questionnaire was used to obtain data from 57 Earth science researchers worldwide. These data served as the bases for a group assessment of geodiversity. The reliability of assessment was evaluated by means of spatially explicit uncertainty analysis. The results showed the efficacy of local spatial multicriteria analysis techniques (L-WLC) used in concert with a global technique (WLC) on the example of geodiversity assessment for Karkonosze National Park in southwestern Poland.

Rankings analysis with the Pareto method

Rankings compare the performance of organizations. In many cases, rankings provide a good assessment of successful or-ganizations. However, rankings often generate controversy and debate since they support the making decisions. A ranking is a weighted linear combination of indicators, and the weights assigned to each of the indicators can lead to different rank orders. In most cases, rankings are used as a tool to support making decisions, such as resource allocation; therefore, these decisions can be affected by the assignment of such weights. In this article, we analyze the behavior of a ranking and the weights; simulations are used to calculate the change in the order of the equally weighted ranking and of the randomly weighted ranking. In this regard, we present a discussion and ranking design alternatives.

Automated Harmonization of Bass Lines from Bach Chorales: A Hybrid Approach

In this article, a combination of two novel approaches to the harmonization of chorales in the style of J. S. Bach is proposed, implemented, and profiled. The first is the use of the bass line, as opposed to the melody, as the primary input into a chorale-harmonization algorithm. The second is a compromise between methods guided by music knowledge and by machine-learning techniques, designed to mimic the way a music student learns. Specifically, our approach involves learning harmonic structure through a hidden Markov model, and determining individual voice lines by optimizing a Boltzmann pseudolikelihood function incorporating musical constraints through a weighted linear combination of constraint indicators. Although previous generative models have focused only on codifying musical rules or on machine learning without any rule specification, by using a combination of musicologically sound constraints with weights estimated from chorales composed by Bach, we were able to produce musical output in a style that closely resembles Bach's chorale harmonizations. A group of test subjects was able to distinguish which chorales were computer generated only 51.3% of the time, a rate not significantly different from guessing.