Search Results (15)

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article

This study employs convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU) deep learning models to simulate daily streamflow using precipitation data. Two approaches were explored: one without dimension reduction and another incorporating dimensionality reduction technique. Principal component analysis (PCA) was employed for dimensionality reduction, and partial autocorrelation function (PACF) was used to determine time lags. An augmented Dickey–Fuller (ADF) test was utilized to ascertain the stationarity of the data, ensuring optimal model performance. The data were normalized and then partitioned into features and target variables, before being split into training, validation, and test sets. The developed models were tested for their performance, robustness, and stability at three locations along the Neuse River, which is in the Neuse River Basin, North Carolina, USA, covering an area of about 14,500 km². Furthermore, the model’s performance was tested during peak flood events to assess their ability to capture the temporal resolution of streamflow. The results revealed that the CNN model could capture the variability in daily streamflow prediction, as evidenced by excellent statistical measures, including mean absolute error, root mean square error, and Nush–Sutcliffe efficiency. The study also found that incorporating dimensionality reduction significantly improved model performance. Full article

Background: Globally, diabetes mellitus is the most common cause of premature mortality after cardiovascular diseases and tobacco chewing. It is a heterogeneous metabolic disorder characterised by the faulty metabolism of carbohydrates, fats and proteins as a result of defects in insulin secretion or resistance. It was estimated that approximately 463 million of the adult population are suffering from diabetes mellitus, which may grow up to 700 million by 2045. Solanum indicum is distributed all over India and all of the tropical and subtropical regions of the world. The different parts of the plant such as the roots, leaves and fruits were used traditionally in the treatment of cough, asthma and rhinitis. However, the hypoglycaemic activity of the plant is not scientifically validated. Purpose: The present study aimed to evaluate the antioxidant, antidiabetic and anti-hyperlipidaemic activity of methanolic fruit extract of Solanum indicum (SIE) in streptozotocin (STZ) induced diabetic rats. Method: Experimentally, type II diabetes was induced in rats by an i.p. injection of STZ at a dose of 60 mg/kg. The effect of the fruit extract was evaluated at doses of 100 and 200 mg/kg body weight in STZ-induced diabetic rats for 30 days. Result: The oral administration of fruit extract caused a significant (p < 0.05) reduction in the blood glucose level with a more prominent effect at 200 mg/kg. The fruit extract showed dose-dependent α-amylase and α-glycosidase inhibitory activity. It reduced the serum cholesterol and triglyceride levels remarkably in diabetic rats compared to normal. The extract showed the reduced activity of endogenous antioxidants, superoxide dismutase, glutathione peroxidase and catalase in the liver of STZ diabetic rats. Conclusion: The result confirmed that the fruit extract of Solanum indicum showed a dose-dependent blood glucose lowering effect and significantly reduced elevated blood cholesterol and triglycerides. It prevented oxidative stress associated with type II diabetes in STZ rats. Full article

Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB. Full article

It is widely known that land use/land cover (LULC) changes significantly alter watershed hydrology and sediment yields. The impact, especially on erosion and sedimentation, is likely to be exacerbated in regions dominated by high rainfall patterns such as monsoons. This study analyzed the hydrological responses of LULC changes in terms of streamflow (SF) and sediment yield (SY) in a monsoon-dominated tropical watershed, the Periyar River Watershed (PRW) in Kerala, India. This watershed drains an area of 4793 km² characterized by an average monsoon rainfall of 2900 mm from June to November. The watershed hydrology and sediment dynamics were simulated using the Soil and Water Assessment Tool (SWAT) model for the impact assessment at the watershed outlet and the sub-watershed level. Historical LULC data were analyzed for 1988, 1992, 2002, and 2016 using the maximum likelihood method, and future LULC changes were projected for 2030, 2050, 2075, and 2100 using the Markov chain–cellular automata technique. Between 1988 and 2016, the urban area increased by 4.13 percent, while plantation and forest coverage decreased by 1.5 percent. At this rate, by 2100, the urban area is expected to grow by 16.45% while plantations and forest area will shrink by 13.7% compared to 1988. The effects of these changes on SF and SY were found to be minimal at the watershed outlet; however, at the spatial scale of sub-watersheds, the changes varied up to 70% for surface runoff and 200% for SY. These findings highlight the potential impacts of LULC changes in a monsoon-dominated watershed and may contribute to the development of successful LULC-based watershed management strategies for prevention of flooding and sediment loss. Full article

Streambed aggradation and degradation are ways in which a stream will respond to changes in the incoming flow and sediment loads. Several environmental and societal problems are attributed to these channel bed adjustments. Prior studies have extensively used stream power to discern dominant channel processes and establish threshold limits required to trigger channel modifications. However, these studies were constrained by limited datasets and the scope of the applications. The current study used a large dataset of streambed median grain size (D50) across the contiguous U.S. in conjunction with a screening tool to assess the streambed stability for channel erosion and deposition potential. Analysis at the Physiographic Province level indicated major geomorphic changes are highly likely to occur in the Blue Ridge and Pacific Border provinces. Deposition-dominated streams are prominent in the Central Lowland, Great Plains, and Coastal Plain, whereas the Colorado Plateaus and Wyoming Basin have the highest percentage of stable channels. Smoothed spatial maps of stream power indicated the prevalence of high stream power in the Northeast and Pacific Northwest regions of the U.S. Comparison of channel erosion and deposition predictions using the stream power map with actual field calculated aggradation and degradation results yielded a 55% prediction accuracy. Further analysis based on the stream order revealed the association of higher stream power with lower stream orders. Full article

The streambed median grain size (D50) has been an integral part of many sediment transport and stream power equations seeking to characterize stream channel stability conditions. However, its previous usage is constrained by regional applicability, localization of datasets, and a limited number of data points. This study uses a large and geographically diverse data set (n > 2400), from five published sources, to present quantitative information and assess the distribution of D50 data across the contiguous U.S. Spatial distribution was analyzed based on the three regional frameworks: Physiographic Provinces, Level III Ecoregions, and Hydrologic Landscape Regions (HLRs). Gravel was found to be the dominant streambed material in most Physiographic Provinces. Regions with a humid climate, permeable soil, and plateaus exhibit a higher average D50 than regions with other climate, geologic texture, and landscape forms. Further analysis of all data across the U.S. using smoothed spatial maps showed the dominance of sand and fine gravel in streams located in the central and southern U.S., and the dominance of coarse gravel and cobbles in the northeastern U.S. Full article

Land-use transition is one of the most profound human-induced alterations of the Earth’s system. It can support better land management and decision-making for increasing the yield of food production to fulfill the food needs in a specific area. However, modeling land-use change involves the complexity of human drivers and natural or environmental constraints. This study develops an agent-based model (ABM) for land use transitions using critical indicators that contribute to food deserts. The model’s performance was evaluated using Guilford County, North Carolina, as a case study. The modeling inputs include land covers, climate variability (rainfall and temperature), soil quality, land-use-related policies, and population growth. Studying the interrelationships between these factors can improve the development of effective land-use policies and help responsible agencies and policymakers plan accordingly to improve food security. The agent-based model illustrates how and when individuals or communities could make specific land-cover transitions to fulfill the community’s food needs. The results indicate that the agent-based model could effectively monitor land use and environmental changes to visualize potential risks over time and help the affected communities plan accordingly. Full article

In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R²) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world. Full article

Accurate rainfall estimates are important in many hydrologic activities. Rainfall data are retrieved from rain gauges (RGs), satellites, radars, and re-analysis products. The accuracy of gauge-based gridded precipitation products (GbGPPs) relies on the distribution of RGs and the quality of rainfall data records obtained from these. The accuracy of satellite-based precipitation products (SbPPs) depends on many factors, including basin climatology, basin topography, precipitation mechanism, etc. The hydrologic utility of different precipitation products was examined in many developed regions; however, less focused on the developing world. The Huai Bang Sai (HBS) watershed in north-eastern Thailand is a less focused but an important catchment that significantly contributes to the water resources in Thailand. Therefore, this research presents the investigation results of the hydrologic utility of SbPPs and GbGPPs in the HBS watershed. The efficiency of nine SbPPs (including 3B42, 3B42-RT, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, CHIRPS, CMORPH, IMERG, and MSWEP) and three GbGPPs (including APHRODITE_V1801, APHRODITE_V1901, and GPCC) was examined by simulating streamflow of the HBS watershed through the Soil & Water Assessment Tool (SWAT), hydrologic model. Subsequently, the streamflow simulation capacity of the hydrological model for different precipitation products was compared against observed streamflow records by using the same set of calibrated parameters used for an RG simulated scenario. The 3B42 product outperformed other SbPPS with a higher Nash–Sutcliffe Efficiency ($NS{E}_{monthly}>0.55$), while APHRODITE_V1901 ($NS{E}_{monthly}>0.53$) performed fairly well in the GbGPPs category with closer agreements with observed streamflow. In addition, the CMORPH precipitation product has not performed well in capturing observed rainfall and subsequently in simulating streamflow ($NS{E}_{monthly}<0$) of the HBS. Furthermore, MSWEP and CHIRPS products have performed fairly well during calibration; however, they showcased a lowered performance for validation. Therefore, the results suggest that accurate precipitation data is the major governing factor in streamflow modeling performances. The research outcomes would capture the interest of all stakeholders, including farmers, meteorologists, agriculturists, river basin managers, and hydrologists for potential applications in the tropical humid regions of the world. Moreover, 3B42 and APHRODITE_V1901 precipitation products show promising prospects for the tropical humid regions of the world for hydrologic modeling and climatological studies. Full article

Ecosystem-based adaptation (EbA) can potentially mitigate watershed degradation problems. In this study, various EbA measures were evaluated using a bio-physical model called the Soil and Water Assessment Tool (SWAT), in a small, forested watershed named Hui Ta Poe, in the northeastern region of Thailand. The developed watershed model was first used to investigate the effect of various degraded watersheds due to land-use changes on the sediment yield in the study area. The most degraded watershed produced an annual average sediment yield of 13.5 tons/ha. This degraded watershed was then used to evaluate the effectiveness of various EbA measures such as reforestation, contouring, filter strips, and grassed waterways in reducing the sediment yield. Under all individual and combined EbA scenarios analyzed, there was a significant reduction in sediment yield; however, the maximum reduction of 88% was achieved with a combined scenario of reforestation, grassed waterways, and filter strips. Reforestation alone was found to be the second-best option, which could reduce the sediment yield by 84%. Contouring alone was the least effective, with a reduction in sediment yield of only 23%. This study demonstrates the usefulness of implementing EbA measures for sediment management strategies to address watershed degradation, which is a severe problem across the globe. Full article

Changes in land use and land cover can have many drivers, including population growth, urbanization, agriculture, demand for food, evolution of socio-economic structure, policy regulations, and climate variability. The impacts of these changes on water resources range from changes in water availability (due to changes in losses of water to evapotranspiration and recharge) to degradation of water quality (increased erosion, salinity, chemical loadings, and pathogens). The impacts are manifested through complex hydro-bio-geo-climate characteristics, which underscore the need for integrated scientific approaches to understand the impacts of landscape change on water resources. Several techniques, such as field studies, long-term monitoring, remote sensing technologies, and advanced modeling studies have been contributing to better understanding the modes and mechanisms by which landscape changes impact water resources. Such research studies can help unlock the complex interconnected influences of landscape on water resources for quantity and quality at multiple spatial and temporal scales. In this Special Issue, we published a set of eight peer-reviewed articles elaborating on some of the specific topics of landscape changes and associated impacts on water resources. Full article

The frequency and severity of floods have been found to increase in recent decades, which have adverse effects on the environment, economics, and human lives. The catastrophe of such floods can be confronted with the advance prediction of floods and reliable analyses methods. This study developed a combined flood modeling system for the prediction of floods, and analysis of associated vulnerabilities on urban infrastructures. The application of the method was tested on the Blue River urban watershed in Missouri, USA, a watershed of historical significance for flood impacts and abundance of data availability for such analyses. The combined modeling system included two models: hydrodynamic model HEC-RAS (Hydrologic Engineering Center—River Analysis System) and hydrologic model SWAT (Soil and Water Assessment Tool). The SWAT model was developed for the watershed to predict time-series hydrograph data at desired locations, followed by the setup of HEC-RAS model for the analysis and prediction of flood extent. Both models were calibrated and validated independently using the observed data. The well-calibrated modeling setup was used to assess the extent of impacts of the hazard by identifying the flood risk zones and threatened critical infrastructures in flood zones through inundation mapping. Results demonstrate the usefulness of such combined modeling systems to predict the extent of flood inundation and thus support analyses of management strategies to deal with the risks associated with critical infrastructures in an urban setting. This approach will ultimately help with the integration of flood risk assessment information in the urban planning process. Full article

A field experiment consists of conservation agriculture (CA) and conventional tillage (CT) practices were set up in two areas, Robit and Dangishta, in sub-humid Ethiopian highlands. Irrigation water use, soil moisture, and agronomic data were monitored, and laboratory testing was conducted for soil samples, which were collected from 0 to 40 cm depth before planting and after harvest during the study period of 2015–2017. Calculation of crop coefficient (Kc) revealed a significant decrease in K_c values under CA as compared to CT. The result depicted that CA with a drip irrigation system significantly (α = 0.05) reduced K_c values of crops as compared to CT. Specifically, 20% reductions were observed for onion, cabbage, and garlic under CA whereas 10% reductions were observed for pepper throughout the crop base period. Consequently, irrigation water measurement showed that about 18% to 28% of a significant irrigation water savings were observed for the range of vegetables under CA as compared to CT. On the other hand, the results of soil measurement showed the CA practice significantly (α = 0.05) increased soil moisture (4%, 7%, 8%, and 10% increment for onion, cabbage, garlic, pepper) than CT practice even if irrigation input was small in CA practice. In addition, CA was found to improve the soil physico-chemical properties with significant improvement on organic matter (10%), field capacity (4%), and total nitrogen (10%) in the Dangishta experimental site. CA with drip irrigation is evidenced to be an efficient water-saving technology while improving soil properties to support sustainable intensification in the region. Full article

Will soil organic carbon (SOC) and yields increase for conservation management systems in tropical zones in response to the next 100 years? To answer the question, the Environmental Policy Integrated Climate (EPIC) model was used to study the effects of climate change, cropping systems, conservation agriculture (CA) and conservation tillage management practices on SOC and crop productivity in Kampong Cham, Cambodia. The EPIC model was successfully calibrated and validated for crop yields, biomass, SOC and nitrogen based on field data from a five-year field experiment. Historical weather (1994–2013) was used for baseline assessment versus mid-century (2046–2064) and late-century (2081–2100) climate projections generated by the Geophysical Fluids Dynamics Laboratory (GFDL) CM2.1 global climate model. The simulated results showed that upland rice yield would increase the most under the B1 scenario in mid-century for all treatments, followed by soybean and maize. Cassava yield only increased under CA treatment when cultivated as a continuous primary crop. Carbon sequestration was more sensitive to cropping systems and crop rotation than climate change. The results indicated that the rotated CA primary crop (maize) systems should be prioritized for SOC sequestration as well as for increasing crop productivity. In addition, rice systems may increase SOC compared to soybean and cassava. Full article