The present study is designed to analyse the annual rainfall variability and trend in 30 meteorological stations of the Asir region for the period of 1970–2017 using the Mann-Kendall (MK) test, ...Modified Mann-Kendall (MMK) test, trend free pre-whitening Mann-Kendall (TFPW MK) test, and the innovative trend analysis (ITA). A comparative study among the trend detection techniques was performed using the correlation coefficient. The future rainfall trend based on the historical rainfall pattern was investigated by using detrended fluctuation analysis (DFA). Results of the MK test showed that 20 stations in the study area observed a negative trend, and out of these, nine stations had significant negative trends at the significance level of 0.01. The findings of the MMK test showed that 23 stations recorded negative trends, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the TFPW-MK test, 21 stations observed a negative trend, and among these, 10 stations had significant negative trends at the significance of 0.01. ITA detected 25 stations observing a negative trend, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the tests and their performance, the MMK test appeared as the best performing technique among the MK test family, while ITA appeared as the best trend detection technique among the four techniques because it outperformed (
p
< 0.01) the others. Results of DFA showed that 23 stations (10 were significant) had recorded declining future rainfall trends based on past trends. The results of the present study would help the planners and policy makers to make accurate and easy decisions on irrigation, climatic, and water resource management in the Asir region of Saudi Arabia.
Groundwater is the most important source of drinking waters supply in the National Capital Territory (NCT) of, New Delhi, India. A diverse geological and topographical set up along with the fast ...growing population and anthropogenic activities has created a need of groundwater quality assurance for drinking and domestic water supply in the region. The major hydro-geochemical process and impacts of anthropogenic activities can be deciphered using multivariate statistical analysis, conventional graphical plots and saturation indices. Groundwater samples were collected from 170 locations spread over entire region and were analysed for a total of 12 water quality physico-chemical parameters. It is observed that the groundwater is neutral to alkaline in nature with electrical conductivity (EC) value ranging from 460 to 8980μs/cm. Chemometric analysis was performed along with geochemical modeling. The 3 clusters obtained through HCA were clearly differentiated based on their chemical characteristics i.e. concentration of major ions. High concentration of nitrate (NO3−) and fluoride (F−) exceeding WHO standards was found in 29% and 27% of the water samples respectively. It is observed that semi-arid climatic conditions along with rock-water interaction, weathering and ion-exchange are the major factors controlling groundwater quality in the region. Oversaturation of fluorite and gypsum has resulted into high concentration of F− in study area. It is found that the results from statistical and geochemical models compliment the findings using conventional plots and are able to decipher comprehensive geochemistry of groundwater in the region.
•PCA, HCA and DA performed of 170 groundwater samples spread in entire region.•PCA generated 4 principal components explaining 76% of variance in the data.•3 cluster were obtained on performing HCA based on electrical conductivity values.•The values of Wilk's Lambda and Chi square indicates DA is statistically significant.•Fluoride and nitrate contamination found in 27% and 29% of samples respectively.
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•Landscape fragmentation probability was analyzed using bagging, random forest (RF), and random subspace (RSS) for the first time.•Twelve class level and landscape level matrices were ...considered in this study.•Bagging has achieved the highest precision (0.864) followed by RSS (0.864) and RF (0.819).•About 49% of the land cover has high to very high probability to be fragmented.
Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs to be addressed due to global environmental change. Many studies have employed remote sensing data for assessing LULC changes, however, the investigation of fragmentation probability modeling is still scarce in the existing literature. Thus, the coupling of bagging, random forest (RF), random subspace (RSS), and their ensemble model with multi-temporal datasets within the GIS environment makes it possible to model the fragmentation probability of LULC in the Teesta River Basin (TRB), Bangladesh. The number of patch (NP), edge density (ED), largest patch index (LPI), contagion index (%) (CONTAG), aggregation index (AI), perimeter area ratio (P/A ratio), the class area (CA), percentage of landscape (PLAND), patch density (PD), total edge (TE), largest shape index (LSI) and total core area (TCA) were the landscape and class matrices, which were derived from the LULC maps using FRAGSTATS software. The machine learning-based sensitivity models, such as decision tree and support vector machine-based feature selection techniques were implemented to explore the influence of the parameters for fragmentation probability modeling. The results showed that water bodies and barren land were substantially decreased by (6.21%), and (14.59%) respectively while the built-up areas increased by 1.45% from 2010 to 2019. Results revealed that the dominance of the agricultural area has been increased as human interference has been elevated in the TRB. However, twelve class-level and landscape matrices were used to delineate the fragmentation probability zone with the aid of bagging, RF, and RSS algorithms. LULC images and fragmentation probability models were validated using the kappa coefficient and the area under curve (AUC) of the receiver operating characteristics (ROC). The validation outcomes depicted that the three models such as bagging (AUC = 0.864), RF (AUC = 0.819), RSS (AUC = 0.859), and ensemble model (AUC = 0.912) have a good capability to appraise the fragmentation probability, and ensemble model has the highest precision level among three models. Nearly 49% (1789 km2) of the LULC was under a high to very high fragmentation potential zone that requires to be protected with direct measures. The results of sensitivity analysis showed that the number of patches significantly influenced the fragmentation probability model, while the largest patch index was the least sensitive parameter for modeling.
The present research aims to build a unique ensemble model based on a high-resolution groundwater potentiality model (GPM) by merging the random forest (RF) meta classifier-based stacking ensemble ...machine learning method with high-resolution groundwater conditioning factors in the Bisha watershed, Saudi Arabia. Using high-resolution satellite images and other secondary sources, twenty-one parameters were derived in this study. SVM, ANN, and LR meta-classifiers were used to create the new stacking ensemble machine learning method. RF meta classifiers were used to create the new stacking ensemble machine learning algorithm. Each of these three models was compared to the ensemble model separately. The GPMs were then confirmed using ROC curves, such as the empirical ROC and the binormal ROC, both parametric and non-parametric. Sensitivity analyses of GPM parameters were carried out using an RF-based approach. Predictions were made using six hybrid algorithms and a new hybrid model for the very high (1835–2149 km
2
) and high groundwater potential (3335–4585 km
2
) regions. The stacking model (ROCe-AUC: 0.856; ROCb-AUC: 0.921) beat other models based on ROC's area under the curve (AUC). GPM sensitivity study indicated that NDMI, NDVI, slope, distance to water bodies, and flow accumulation were the most sensitive parameters. This work will aid in improving the effectiveness of GPMs in developing sustainable groundwater management plans by utilizing DEM-derived parameters.
The increasing urban heat island intensity (UHII)is a matter of concern for the sustainable urban planning and maintaining urban thermal comfort in the metropolitan cities of developing countries ...like India. Therefore, in the current research, the land use/land cover (LU/LC) change and its impact on the surface UHI intensity (SUHII) and urban thermal comfort has been analyzed using Landsat datasets and geographically weighted regression (GWR) in Delhi metropolitan city. The result shows that the built-up area has increased from 315.18 to 720.24 sq. km in Delhi during 1991 to 2018 while other LU/LC types have declined. This has resulted in a substantial increase in LST and SUHII. The minimum, maximum and mean SUHII has increased by 1.26 °C, 4.6 °C and 1.18 °C during 1991 to 2018 and hence, the thermal comfort has declined in the city. The GWR analysis showed that the performance of GWR model was very good in showing the association between LU/LC and SUHII and the LU/LC pattern has significant impact on SUHII. The outcome of this research can be utilized for the formulation of SUHI mitigation strategies and maintaining urban thermal comfort in Delhi and cities with similar geographical conditions.
•SUHII and urban thermal comfort has been analyzed in response to changing LU/LC pattern in Delhi during 1991 to 2018.•The built-up area is doubled, whereas cropland decreased by half in Delhi during 1991 to 2018.•Mean SUHII has increased to about 1.18 °C in Delhi in last 27 years.•Urban thermal comfort has declined in Delhi because of increase in about 9 °C LST and 1.16 °C in SUHII in last 27 years.•The GWR model shows significant association (R2=0.48) between LU/LC and SUHII in Delhi.
Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting and recharge structures, groundwater potential, runoff, watershed prioritization ...through morphometric analysis. The management and conservation of watershed require scientific evaluation so that appropriate strategies can be implemented by the policymakers and stakeholders. This study evaluates the Parvati River Basin for prioritization of sub-watershed. We use analytical hierarchical approach with pairwise comparison matrix for ranking the parameters which has been derived through morphometric analysis of the watersheds. The linear (stream order, stream length, mean, stream length, stream length ratio, bifurcation ratio, rho coefficient), relief (basin relief, relief ratio, ruggedness number) and areal morphometric parameters (drainage density, stream frequency, texture ratio, form factor, circulatory ratio, elongation ratio, length of overland flow, constant channel maintenance) have been used for prioritization of watershed in the Basin. The results revealed that the basin comprises of complex topography with mountainous and hilly relief structures which results into high run-off. The prioritization of sub-watershed suggests that the largest watershed comprising of 36.09% total watershed area falls into medium category for water potential. The total area under very high to high category is 5.36% and 8.44% respectively. The relationship between morphometric parameter and watershed prioritization shows that the sub-watersheds SW1 and SW8 falls under very high to high category having high water holding capacity.
One of the main issues with solid waste management is finding appropriate sites for landfill. Non-scientific and inappropriate disposal practices have a negative impact on the environment which ...affects the quality of life. The study provides an integrated framework with a focus on structuring the decision-making process for the landfill suitability site map. This could be determined by the use of proper data collection, criterion weighting and normalization. In order to understand the procedures that affect the suitability of landfill sites, the integrated GIS-based fuzzy-AHP-MCDA method was implemented to appropriate landfill site for Abha-Khamis-Mushyet located in Aseer region Following the extensive literature review and expert opinion, 10 themes were selected for this study such as drainage density, land use/land cover (LULC), slope, elevation, lineament density, normalized difference vegetation index (NDVI), rainfall, distance from the airport, distance from road, and geology. These themes have been developed through RS (remote sensing) and conventional data. Subsequently, potential landfill sites were identified and divided into five classes: very low suitable (fuzzy value 0.20–0.45), low suitable (0.46–0.55), moderately suitable (0.56–0.65), high suitable (0.66–0.75), and very high suitable (0.76–0.92). According to the statistical analysis, 23.91% and 3.67% of the total area were within a very good and good landfill area, while 38.14% and 22.84% accounted for the moderate and poor suitable zone, respectively. As a quality-based site, the existing two landfill sites were located over a very low suitable and low suitable potential area while one landfill site was located over the high suitable∙ The spatial variance of high and very high potential landfill site zones found in the north-eastern, east-central and south-eastern parts of the watershed. The sensitivity analysis was performed to determine the efficacy of each parameter and reveals that the effective weights for each theme differ slightly from the theoretical weight assigned to the landfill site suitability zone. This technique and its findings can provide an appropriate guideline to assist hydrogeologists, engineers, regional planners, and decision-makers in selecting an optimal landfill site in the future.
Landslides and other disastrous natural catastrophes jeopardise natural resources, assets, and people’s lives. As a result, future resource management will necessitate landslide susceptibility ...mapping (LSM) using the best conditioning factors. In Aqabat Al-Sulbat, Asir province, Saudi Arabia, the goal of this study was to find optimal conditioning parameters dependent hybrid LSM. LSM was created using machine learning methods such as random forest (RF), logistic regression (LR), and artificial neural network (ANN). To build ensemble models, the LR was combined with RF and ANN models. The receiver operating characteristic (ROC) curve was used to validate the LSMs and determine which models were the best. Then, utilising random forest (RF), classification and regression tree (CART), and correlation feature selection, sensitivity analysis was carried out. Through sensitivity analysis, the most relevant conditioning factors were determined, and the best model was applied to the important parameters to build a highly robust LSM with fewer variables. The ROC curve was also used to evaluate the final model. The results show that two hybrid models (LR-ANN and LR-RF) were predicted the very high as 29.67–32.73 km
2
and high LS regions as 21.84–33.38 km
2
, with LR predicting 22.34km
2
as very high and 45.15km
2
as high LS zones. The LR-RF appeared as best model (AUC: 0.941), followed by LR-ANN (AUC: 0.915) and LR (AUC: 0.872). Sensitivity analysis, on the other hand, allows for the exclusion of aspects, hillshade, drainage density, curvature, and TWI from LSM. The LSM was then predicted using the LR-RF model based on the remaining nine conditioning factors. With fewer variables, this model has achieved greater accuracy (AUC: 0.927). This comes very close to being the best hybrid model. As a result, it is strongly advised to choose conditioning parameters with caution, as redundant parameters would result in less resilient LSM. As a consequence, both time and resources would be saved, and precise LSM would indeed be possible.
Geopolymer concrete (GPC) is a new material in the construction industry, with different chemical compositions and reactions involved in a binding material. The pozzolanic materials (industrial waste ...like fly ash, ground granulated blast furnace slag (GGBFS), and rice husk ash), which contain high silica and alumina, work as binding materials in the mix. Geopolymer concrete is economical, low energy consumption, thermally stable, easily workable, eco-friendly, cementless, and durable. GPC reduces carbon footprints by using industrial solid waste like slag, fly ash, and rice husk ash. Around one tonne of carbon dioxide emissions produced one tonne of cement that directly polluted the environment and increased the world’s temperature by increasing greenhouse gas production. For sustainable construction, GPC reduces the use of cement and finds the alternative of cement for the material’s binding property. So, the geopolymer concrete is an alternative to Portland cement concrete and it is a potential material having large commercial value and for sustainable development in Indian construction industries. The comprehensive survey of the literature shows that geopolymer concrete is a perfect alternative to Portland cement concrete because it has better physical, mechanical, and durable properties. Geopolymer concrete is highly resistant to acid, sulphate, and salt attack. Geopolymer concrete plays a vital role in the construction industry through its use in bridge construction, high-rise buildings, highways, tunnels, dams, and hydraulic structures, because of its high performance. It can be concluded from the review that sustainable development is achieved by employing geopolymers in Indian construction industries, because it results in lower CO2 emissions, optimum utilization of natural resources, utilization of waste materials, is more cost-effective in long life infrastructure construction, and, socially, in financial benefits and employment generation.
Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the ...semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.
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