Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable ...landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future.
•An efficient constrained Rao algorithm is established for continuous truss sizing.•The method, named FBSmRao, always provides fully feasible design.•FBSmRao is simple and requires no ...algorithm-specific control parameter to perform.•FBSmRao successfully optimizes four benchmark trusses with static constraints.•The optimized designs are competitive in terms of quality and robustness.
Newly developed optimization algorithms, the Rao algorithms, are utilized for optimal truss sizing in this study. In particular, a modified Rao algorithm that takes advantage of Rao-1and Rao-2 algorithms is proposed. The modified algorithm retains the main feature of Rao algorithms, i.e., simplicity and parameter-freeness. The modified Rao algorithm is then hybridized with the Feasible Boundary Search (FBS) technique to form a constrained Rao algorithm, named FBSmRao. The applicability of the original Rao algorithms, including Rao-1 and Rao-2, and the proposed FBSmRao for truss sizing optimization is validated through four benchmark design problems with continuous variables. The results show that Rao algorithms can be used to obtain optimum weight for trusses. Moreover, the proposed FBSmRao outperforms the original Rao algorithms in terms of solution quality and convergence speed. Comparing with some other metaheuristics, including ones using FBS, FBSmRao can obtain competitive designs, especially for large-scale structures.
Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland ...using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.
To assess the role of in-flight transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we investigated a cluster of cases among passengers on a 10-hour commercial flight. ...Affected persons were passengers, crew, and their close contacts. We traced 217 passengers and crew to their final destinations and interviewed, tested, and quarantined them. Among the 16 persons in whom SARS-CoV-2 infection was detected, 12 (75%) were passengers seated in business class along with the only symptomatic person (attack rate 62%). Seating proximity was strongly associated with increased infection risk (risk ratio 7.3, 95% CI 1.2-46.2). We found no strong evidence supporting alternative transmission scenarios. In-flight transmission that probably originated from 1 symptomatic passenger caused a large cluster of cases during a long flight. Guidelines for preventing SARS-CoV-2 infection among air passengers should consider individual passengers' risk for infection, the number of passengers traveling, and flight duration.
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and ...Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
Choosing downscaling techniques is crucial in obtaining accurate and reliable climate change predictions, allowing for detailed impact assessments of climate change at regional and local scales. ...Traditional statistical methods are likely inefficient in downscaling precipitation data from multiple sources or complex data patterns, so using deep learning, a form of nonlinear models, could be a promising solution. In this study, we proposed to use deep learning models, the so‐called long short‐term memory and feedforward neural network methods, for precipitation downscaling for the Vietnamese Mekong Delta. Model performances were assessed for 2036–2065 period, using original climate projections from five climate models under the Coupled Model Intercomparison Project Phase 5, for two Representative Concentration Pathway scenarios (RCP 4.5 and RCP 8.5). The results exhibited that there were good correlations between the modelled and observed values of the testing and validating periods at two long‐term meteorological stations (Can Tho and Chau Doc). We then analysed extreme indices of precipitation, including the annual maximum wet day frequency (Prcp), 95th percentile of precipitation (P95p), maximum 5‐day consecutive rain (R5d), total number of wet days (Ptot), wet day precipitation (SDII) and annual maximum dry day frequency (Pcdd) to evaluate changes in extreme precipitation events. All the five models under the two scenarios predicted that precipitation would increase in the wet season (June–October) and decrease in the dry season (November–May) in the future compared to the present‐day scenario. On average, the means of multiannual wet season precipitation would increase by 20.4 and 25.4% at Can Tho and Chau Doc, respectively, but in the dry season, these values were projected to decrease by 10 and 5.3%. All the climate extreme indices would increase in the period of 2036–2065 in comparison to the baseline. Overall, the developed downscaling models can successfully reproduce historical rainfall patterns and downscale projected precipitation data.
Statistical downscaling of precipitation using machine learning of long short‐term memory and feedforward neural network.
Successful downscaling of precipitation was obtained from five global climate models for the Vietnamese Mekong Delta.
Increasing future precipitation in the wet season and decreasing in the dry season in both RCP 4.5 and RCP 8.5 scenarios.
Six extreme indices of precipitation were analysed with a robust trend of increased flooding and drought probability.
The outputs of statistical downscaling could be used for further basin assessments and beneficial for water resources management.
Regional climate change (CC) and land use changes (LUCs) can significantly influence the hydrological processes at watershed scale. Different studies have investigated the impact of climate change in ...the Indus Basin. However, there is a need to investigate the impact of environmental changes on the regional hydrology over a complex topographic region. This study quantitatively assesses the relative contributions of CC and LUC on runoff alterations across Gilgit watershed by using multivariable calibration approach using the Soil and Water Assessment Tool (SWAT). Mann–Kendall (MK) and Pettitt tests are applied to identify the trends and changes in runoff and climatic variables during 1985–2013. The supervised classification is performed to acquire land use maps and other quantitative details required for the analyses. Moreover, Indicators of Hydrologic Alterations (IHA) analyses were performed for the first time in the Gilgit watershed to investigate the impact of CC and LUCs during the pre- and post-impact periods. The results demonstrated that precipitation, temperature, and runoff of the Gilgit watershed presented significant increasing trends. The change point using Pettitt test is depicted in 1999, 1995, and 1998, respectively. The mean annual increasing rate of precipitation, temperature, and runoff is 4.92 mm/year, 0.04 °C/year, and 2.60 m
3
/year, respectively. SWAT model performed well and the relative attributed contribution of CC to runoff change is 97.22% and it is 2.78% for LUC. The IHA results showed that runoff has significantly increased in post-impact (1999–2013) as compared to pre-impact (1985–1998), which was further confirmed by analyzing the IHA results using percent bias (PBIAS). Significant overestimation of runoff (higher runoff in post-impact period) was observed in the wet (maximum runoff) season. This study demonstrated that the high contribution of CC to runoff change is mainly due to the change in climate variables and global warming trends.
Salinity intrusion in the Vietnamese Mekong Delta (VMD) has been exacerbated significantly in recent years by the changing upstream inflows, sea level rise resulting from climate change, and ...socioeconomic development activities. Despite significant damage to agricultural production and freshwater supplies, quantitative assessments of future flows and salinization remain limited due to lack of observation data and modelling tools to represent a highly complex hydraulic network. In this study, we combine 1D-MIKE 11 and 2D-MIKE 21 hydrodynamic models to simulate future flows, water level and salinity intrusion in the Hau River—one main river branch in the Mekong Delta. Future hydrological changes are simulated under multiple scenarios of upstream inflow changes, climate change and sea level rise for the 2036–2065 period. We first use the 1D-MIKE 11 to simulate the flow regime throughout the whole VMD using upstream discharges, outlet water levels and rainfall data as boundary conditions. Output from this step is then used to force the 2D-MIKE 21 model to estimate flow velocity, water level and salinity concentration in the Hau River, focusing on the salinization-prone section between Can Tho, Dinh An, and Tran De estuaries. Simulation results show that salinization will increase substantially, characterized by (1) higher salinity intrusion length under spring tide from 6.78% to 7.97%, and 8.62% to 10.89% under neap tide; and (2) progression of the salinity isohalines towards the upper Mekong Delta, from 3.29 km to 3.92 km for 1 practical salinity unit (PSU) under spring tide, and 4.36 km to 4.65 km for 1 PSU concentration under neap tide. Additionally, we found that salinity intrusion will make it more difficult to re-establish the freshwater condition in the estuary in the future. In particular, the flushing time required to replace saltwater with freshwater at the estuaries tends to increase to between 7.27 h for maximum discharge of 4500 m3/s and 58.95 h for discharge of 400 m3/s under the most extreme scenario. Increasing salinization along the Hau River will have important consequences for crop production, freshwater supplies and freshwater ecosystems, therefore requiring timely adaptation responses.
Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous ...landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.
This paper investigates how trade openness affects wage inequality within and between trading countries under a new framework that incorporates the endogenous technology choice assumption. This ...assumption implies that as well as making a labor choice, the firms in our model simultaneously choose to adopt different technology compositions, rather than simply utilizing the standard constant technology, as assumed in most previous researches. Theoretically, we find that the endogenous technology choice partially absorbs the negative effect of the unskilled-skilled labor supply ratio on the relative wage within a country. Furthermore, compared with the standard constant technology model, the calibration of the new framework using data from 52 countries yields qualitatively and quantitatively different results for the impacts of transport costs on the relative wage between the two countries. Specifically, there are cases in which this difference generates contradictory interpretations of the effect of a transport cost reduction on wage inequality. For several pairs of countries, the wage differential between two countries becomes more evident in response to trade openness in the standard constant technology model, whereas in the endogenous technology choice model, the gap becomes narrower.
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