 Subjects -> METEOROLOGY (Total: 106 journals)
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- Correction: Comparative studies of enhancing oil recovery optimization for
optimum oil feld development-
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PubDate: 2023-06-01
- Correction to: Modeling rock slope stability using kinematic, limit
equilibrium and finiteelement methods along Mertule Maryam–Mekane Selam
road, central Ethiopia-
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PubDate: 2023-06-01
- Correction to: Non-linear modelling of adsorption isotherm and kinetics of
chromium(VI) and celestine blue attenuation using a novel
poly(curcumin-citric acid)/MnFe2O4 nanocomposite-
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PubDate: 2023-06-01
- Managing groundwater demand through surface water and reuse strategies in
an overexploited aquifer of Indian Punjab-
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Abstract: Groundwater sustainability is one of the most critical issues to the State of Punjab, India. In this research, a numerical groundwater flow model (MODFLOW) was employed to simulate flow and groundwater levels in the Sirhind Canal Tract of Punjab between 1998 and 2030. Historical groundwater patterns were calibrated using reported groundwater data from 1998 to 2013 for aquifer parameters viz. hydraulic conductivity and specific yield. Thereafter, calibrated flow simulated model was validated for the years 2013–2018. Twelve possible strategies, including three irrigation conditions and four pumping scenarios, were postulated to evaluate the performance of groundwater resources through to 2030. During the study, it was found that if current groundwater abstraction continues there will be further steep decline of 21.49 m in groundwater level by 2030. Findings also suggest that canal water supplies will be beneficial to reverse groundwater level decline and help to increase the water level by 11% above that in year 2018. The projected increases in water level will reduce energy demand leading to reduced CO2 emissions of approximately 966.6 thousand tonnes by 2030.
PubDate: 2023-06-01
- Effects of dust and meteorological variables on temperature inversion over
Kuwait-
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Abstract: Temperature inversion plays an essential role in the atmospheric thermal stability that affects the transportation and dispersion of pollutants. Little is known about the relationship between temperature inversion and meteorology in hot and dry arid climates. The relationship between temperature inversion with dust storms and meteorology is investigated in this study, in which the State of Kuwait has a hot arid environment. Hourly data on temperature inversion, dust storms, and meteorological variables (temperature, relative humidity, wind speed, visibility, and solar radiation) were collected for 5 years (2013–2017). The Meteorological Temperature Profiler (MTP-5) model was used to calculate temperature inversions. Spearman’s non-parametric correlations (rs) were used to determine the relationship between temperature inversions and the five meteorological variables, while Fisher’s Exact Chi-square tests (χ2) were used to determine the relationship between temperature inversions and dust storms. The results showed that temperature inversions are very common in Kuwait. Temperature inversions generally develop after sunset and diminish after sunrise and are significantly correlated with high temperature and low relative humidity (p ≤ 0.01). Temperature inversions were shown to be poorly correlated with dust storms (χ2 = 0.794, rs = 0.016). These findings indicate that nighttime, particularly during the spring and summer, creates good conditions for temperature inversions in arid regions and can lead to higher pollutant concentrations. These findings have implications for improving our understanding of air pollution and the contributing factors in hot arid environments.
PubDate: 2023-06-01
- Modeling the effects of land use/land cover changes on water requirements
of Urmia Lake basin using CA-Markov and NETWAT models-
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Abstract: Urmia Lake, the largest inland lake in Iran, is facing a severe drying scenario, with dire consequences for the whole region. The rapid expansion of agricultural activities in the Urmia Lake basin has predominately led to tremendous pressure on the limited water resources, which has accelerated the drying process of this lake. The objective of this study is to analyze the spatio-temporal dynamics of land use/land cover (LULC) (2000–2020) and simulate agricultural expansion (2030 and 2040) in the Urmia Lake basin. Support Vector Machine (SVM)-based classification approach was used on Landsat satellite imagery from 2000, 2010, and 2020 to derive respective LULC layers. Cellular Automata (CA)-Markov and Land Change Modeler (LCM) were employed to simulate and assess future agricultural growth and land cover changes. Furthermore, the water requirement of agricultural activities was estimated with the NETWAT model. The results showed that the areas covered by irrigated agriculture and gardens are projected to experience a significant increase. These findings indicated that the actual LULC change during 2000–2020 was 68,802 ha of garden growth (174% change), while the simulated change was expected to be 127,613 ha by 2040. Moreover, the statistical result showed an increase of irrigated and rain-fed agricultural lands by 147,948 ha (55.98%) and 356,372 ha (145.69%), respectively, by 2040. Adopting the NETWAT model, this study suggests that the changes in LULC of the region will increase the water requirement of agriculture activities from 1500 billion cubic meters in 2000 to more than 4100 billion cubic meters in 2040.
PubDate: 2023-06-01
- Modeling and optimal control of monkeypox with cost-effective strategies
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Abstract: In this work, we develop and analyze a deterministic mathematical model to investigate the dynamics of monkeypox. We examine the local and global stability of the basic model without control variables. The outcome demonstrates that when the reproduction number \({\mathcal {R}}_{0}<1\) , the model’s disease-free equilibrium would be locally and globally asymptotically stable. We further analyze the effective control of monkeypox in a given population by formulating and analyzing an optimal control problem. We extend the basic model to include four control variables, namely preventive strategies for transmission from rodents to humans, prevention of infection from human to human, isolation of infected individuals, and treatment of isolated individuals. We established the necessary conditions for the existence of optimal control using Pontryagin’s maximal principle. To illustrate the impact of different control combinations on the spread of monkeypox, we use the fourth-order Runge–Kutta forward–backward sweep approach to simulate the optimality system. A cost-effectiveness study is conducted to educate the public about the most cost-effective method among various control combinations. The results suggest that, of all the combinations considered in this study, implementing preventive strategies for transmission from rodents to humans is the most economical and effective among all competing strategies.
PubDate: 2023-06-01
- Tono basin climate modeling, the potential advantage of fully coupled
WRF/WRF-Hydro modeling System-
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Abstract: The climate and hydrological cycle are influenced by the relationships between land-surface characteristics and the atmosphere. According to this research, a hydrological–atmospheric coupled model adds value to river basin climate modeling by determining if it is useful. The Tono basin in Ghana's Ghana was the focus of the research, which looked at the climate of West Africa as a whole. Simulations of WRF and WRF-Hydro (WRF/WRF-Hydro) were conducted separately and together. Both runs use the same physical parameterizations. There were two methods used to evaluate the model's ability to predict precipitation and temperature in the basin: statistical analysis and spatial bias analysis. The coupled model outperformed the uncoupled model in terms of precipitation and temperature prediction. Coupling models can be used to simulate sub-grid hydrological basin features, like the Tono irrigation dam, as a result of this study.
PubDate: 2023-06-01
- Recurrent neural networks for rainfall-runoff modeling of small Amazon
catchments-
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Abstract: The insufficiency of hydrological time series prejudices the management of water resources in the Amazon, especially in small catchments. Thus, for the first time, through the Nonlinear Autoregressive Recurrent Neural Network with Exogenous Inputs (RNN-NARX), an attempt is made to simulate daily streamflow time series with a temporal resolution of 365 days, for five small Amazon catchments. A sensitivity analysis of the models was also performed to identify the lowest temporal resolution of the input to obtain satisfactory results. Since it is data-driven model, it is expected that these models have the ability to reproduce learning characteristics with less temporal variability, and make it possible to estimate daily streamflow time series with 365-day temporal resolution. For this objective, daily lagged rainfall and streamflow data were implemented with the support of the Cross-Correlation Function (CCF) and partial autocorrelation function (PACF) at the 5% significance level. Based on five statistical criteria, satisfactory results were obtained with supervised training based on 2 years of rainfall and streamflow data in four of the five analyzed basins (Igarapé of Prata, Piranhas River, Caeté River and Capivara River). According Garson’s algorithm, lagged rainfall is important for these simulations. In general, there are lower percentage errors in the dry periods, and overestimation of floods. In the practical context, the models developed and analyzed are applicable, mainly, for the simulation of average and minimum daily streamflows of small catchments in the Amazon, becoming a tool that should be used for sustainable evaluation purposes of the water availability of these catchments. However, in the case of simulating floods, it is necessary to apply hourly and lagged rainfall and streamflow data to the models.
PubDate: 2023-06-01
- Development of ANN model for the prediction of discharge coefficient of an
arced labyrinth side weir-
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Abstract: Side weirs, referred to as lateral weirs, are flow diversion hydraulic structures frequently used in canal systems, irrigation-drainage systems, and urban sewage systems as a head regulator of distributaries and escapes. Previous studies have mainly focused on side weirs that are rectangular or triangular shape. The present study investigated the hydraulic effects of an arced labyrinth side weir with triangular keys. It is necessary to establish the discharge coefficient equation for the side weir in order to estimate the outflow over an arced labyrinth. To estimate the discharge coefficient of the arced labyrinth side weir, a thorough laboratory investigation was carried out. Non-linear regression and artificial neural network (ANN) approaches have been used to analyse the data and create new models. Based on a number of performance metrics, it was shown that the suggested ANN model (R = 0.9235 and RMSE = 0.0451) has a greater accuracy than the non-linear regression model (R = 0.7206 and RMSE = 0.0521). Additionally, it was found that the discharge coefficient calculated using ANN is more precise than the results of regression equation.
PubDate: 2023-06-01
- An evaluation of satellite precipitation downscaling models using machine
learning algorithms in Hashtgerd Plain, Iran-
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Abstract: Satellite precipitation products are one of the sources of precipitation estimates. However, their spatial resolution is often too coarse for use in local areas or parameterization of meteorological and hydrological models at basin and plain scales. Therefore, the main objective of this study is to evaluate the accuracy and uncertainty analysis of the downscaled monthly precipitation modeling of the CHIRPS satellite product based on rain gage station data. Five high-resolution ancillary data were used for this purpose including land surface temperature (LST), leaf area index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) extracted from the MODIS/Terra satellite database, and elevation data obtained from the Shuttle Radar Topography Mission (SRTM) of NASA JPL satellite database. In addition, precipitation data were extracted from CHIRPS satellite data. Four machine learning models were used to model the downscaling, including support vector machine (SVM), gradient tree boost (GTB), random forest (RF), and classification and regression tree (CART). For accuracy evaluation, root mean square error (RMSE), Bias, multiplicative bias (mBias), correlation coefficient (CC), and optimum index factor (OIF) error estimators were applied. The results of the accuracy evaluation results showed that the CART and GTB models with the best performance according to the error estimators were the best models for stations at lower altitudes and latitudes (Karim Abad, Najm Abad, and Somea) and stations at higher altitudes and latitudes (Valian, Sorheh, and Fashand), respectively. The uncertainty analysis was performed using the bootstrap method. The results showed that the CART and GBT models had a more reliable estimate and lower uncertainty than the other models. This study highlights the power of the Google Earth Engine and machine learning algorithms in downscaling modeling to improve the resolution of satellite precipitation data.
PubDate: 2023-06-01
- Modeling the potential distribution of two immortality flora in the
Philippines: Applying MaxEnt and GARP algorithms under different climate
change scenarios-
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Abstract: Climate change shifts the distribution of socioeconomically important medicinal species such as Ganoderma lucidum and Gynostemma pentaphyllum, renowned as immortality mushroom and herb, respectively. To predict their ecological niche and potential distribution in the Philippines, species distribution modeling (SDM) was performed using two algorithms under three climate change scenarios: current, and future Shared Socioeconomic Pathways (SSPs) 1–2.6 and 3–7.0 of the EC-Earth3-Veg-LR Earth System Model for 2081–2100. Maximum entropy (MaxEnt) and Genetic Algorithm for Rule Set Production (GARP) yielded acceptable mean Area Under the ROC (Receiver Operating Characteristic) curve (AUC) scores (0.677–0.806). MaxEnt models predict that, under the current scenario, G. lucidum is distributed in low-altitude, open forests with high temperature and precipitation seasonality in mainland Luzon. Meanwhile, G. pentaphyllum is distributed in annually cold and highly diurnal high-altitude mountains across the whole archipelago. Under both future scenarios, based on percent change of very highly suitable areas, G. lucidum is predicted to decrease in suitability (–2.67 to –5.30%) and undergo upward range reduction, while G. pentaphyllum is predicted to increase in suitability (+ 6.75 to + 25.61%) and undergo downward range expansion. However, these migration trends are not evident in GARP models due to its overpredictive nature, mainly due to the use of categorical predictors. Hence, for its conservative predictions, MaxEnt is recommended for presence-only (PO) modeling. These models establish baseline information for local threat assessment and conservation planning for both ‘immortality’ flora. This is the first report of medicinal macrofungus and herb utilizing SDM in the Philippines.
PubDate: 2023-06-01
- Optimization of biomass production from sugar bagasse in anaerobic
digestion using genetic algorithm-
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Abstract: Due to the increasing demand for energy and environmental concerns regarding fossil fuels, the world is seeking renewable energy alternatives. Biofuels derived from biomass are a viable alternative to fossil fuels. To convert organic matter into biogas, anaerobic digestion is a promising biochemical method. Anaerobic digestion technology relies heavily on accurate modeling, optimization, control, and error detection. This paper predicts the effect of each parameter, such as temperature, time, and density, on biogas production using linear and exponential regressions. A genetic algorithm is then used to optimize the resulting relationships. To obtain the optimal values of the outputs, regression relationships were obtained and used to define the objective function. This study compares the optimal values obtained from MSE error with those obtained from exponential regression. Although the validity of the results associated with exponential regression is greater, the optimal values obtained from the linear regression relationship have been better.
PubDate: 2023-06-01
- Groundwater pollution vulnerability assessment in the Assin municipalities
of Ghana using GIS-based DRASTIC and SINTACS methods-
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Abstract: This study has employed the DRASTIC and SINTACS models to assess the groundwater pollution vulnerability potential in the Assin municipalities of Ghana, where agricultural activities, increase in population and industrial activities stand to negatively impact the quality of groundwater, which is the main source of water supply to the populace. Hydrogeological parameters including depth to the water table, aquifer recharge, hydraulic properties of the aquifer, surface topography, and soil properties were utilized in the Geographical Information System (GIS) environment to evaluate the vulnerability of the study area to groundwater pollution. The results indicate that fractured and weathered granites are the main aquifer formations in the area, while the principal soils are mainly clay, clayey sand, loamy and sandy clay, with depth to the water table and hydraulic conductivity ranging between 12–62 m and 0.0031–2.4705 m/day, respectively. In addition, the net recharge range is between 122.80 and 198.20 mm/year, with an average of 163.27 mm/year. DRASTIC model assessment showed that 812.78 km2 (33.62%) of the area has low vulnerability potential with the remaining 1604.22 km2 (66.37%) having moderate vulnerability potential, while 667.76 km2 (27.63%) and 1749.24 km2 (72.37%) of the area were deemed to have low, and moderate vulnerability potentials, respectively, from assessment with the SINTACS model. The moderate groundwater vulnerability zones are generally underlained by undifferentiated biotite granitic rocks, mafic dykes, and the leucogranites, whereas the biotite gneiss and Banket formation underlain the low vulnerable zones. Thus, providing useful information to aid in taking appropriate pollution prevention measures to protect the groundwater resource in the area.
PubDate: 2023-06-01
- A dynamical study on the adverse effects of industrial activities in the
forest and wildlife region through modelling-
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Abstract: A nonlinear mathematical model is designed and analysed to investigate the impact of forest, non-forest-based industries and their pollutants on forest assets and wildlife population in the forest territory. The pollution from forest and non-forest-based industries is major cause for the depletion of forest resources and wildlife population. In the modelling process, it is assumed that pollutants emitted by both types of industries abate the growth rate of forest resources and wildlife population. However, the forest assets are being depleted by forest-based industries directly. Whether excessive expansion of non-forest-based industries and their pollutants are also major responsible for the loss of ecology between forest resources and wildlife population. The model is expressed in the form of nonlinear dynamical systems considering as different variables and equations. The model is analysed by both ways of method like qualitative and quantitative analysis. Qualitative analysis deduces important results and properties of the model like stability region, finding of equilibriums and their stabilities. However, the model is also analysed by quantitatively to obtained numerical results. It is also deduced that the pollutants which are emitted by non-forest-based industries are more vulnerable to ecology.
PubDate: 2023-06-01
- A comparative modeling of landslides susceptibility at a meso-scale using
frequency ratio and analytic hierarchy process models in geographic
information system: the case of African Alpine Mountains (Rif, Morocco)-
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Abstract: Landslides represent a major natural hazard for all countries in the world. The Rif mountains in Morocco suffer from different types of landslides. Some of them are very active and present a significant risk to urban areas and transport systems. Consequently, in terms of sustainable development, landslide susceptibility mapping is essential to assess the levels of danger posed by these phenomena. This study aims at evaluating landslide susceptibility using two different approaches based on a statistical method (Frequency Ratio, FR) and on a heuristic method (Analytic Hierarchy Process, AHP). The second purpose is to compare them to select the most relevant and reproducible one with a view to applying it to areas having a similar geomorphological context. This study includes a precise inventory map representing the spatial distribution of three landslide categories within 892 sites. Rock falls, flows and landslides were studied using field survey and satellite imagery. Nine thematic layers of predisposing factors controlling landslides occurrence were prepared. The final result is presented in the form of six susceptibility maps of rockfalls, flows and landslides for FR and AHP models. The result of the success rates (AUC) indicates that the FR method is better with an AUC of 88% for rock fall, 89% for flows and 87% for landslides, while the AUC is 83%, 84% and 76%, respectively for the AHP method. Moreover, the results indicate which method to use for similar regions to produce indicative mapping and help users select priority areas prone to landslides.
PubDate: 2023-06-01
- Landslide susceptibility mapping using GIS-based statistical and machine
learning modeling in the city of Sidi Abdellah, Northern Algeria-
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Abstract: Landslides are one of the most common and damaging geological hazards that constrain the urban planning and development of many cities in northern Algeria. Therefore, landslide susceptibility maps (LSMs) constitute an essential tool for effective hazard management and long-term development planning in landslide-prone areas. The aim of this work is to prepare and compare the LSMs by applying GIS-based statistical and machine learning models for the new city of Sidi Abdellah and surrounding areas (Northern Algeria). We implemented the statistical models of the frequency ratio (FR), statistical index (SI), and weights of evidence (WoE) models, and the machine learning models represented by a logistic regression (LR) model for landslide susceptibility prediction. An historical landslide inventory map was produced using the interpretation of Google Earth satellite images, available historical records, and geological field investigations. The obtained landslides were randomly divided into the training (70%) and validation (30%) processes. Furthermore, 12 influencing factors for landslide occurrence (including precipitation, slope, elevation, distance to drainage, aspect, land use, density of streams, distance to road, lithology, distance to fault, seismicity, and density of roads) were selected to prepare thematic maps and were considered for susceptibility analysis. Subsequently, landslide susceptibility assessment and mapping are performed by considering the inventoried landslide events and their related predisposing factors using LR, SI, WoE, and FR models in GIS. The accuracy of the four models was verified, validated, and compared using the area under curve (AUC) value of the Receiver Operating Characteristics Curves (ROC) method. The validation results showed that all the used statistical models provided a good accuracy in predicting landslide susceptibility than the machine learning models, with the SI model having a higher AUC value of 80.1% than the WoE (78.2%), FR (78.2%), and LR (64.2%) models. Based on these results, we conclude that the established maps can be used as useful tools for land use planning and risk reduction in the urban area of Sidi Abdellah.
PubDate: 2023-06-01
- Modeling of groundwater quality index by using artificial intelligence
algorithms in northern Khartoum State, Sudan-
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Abstract: In the present study, multilayer perceptron (MLP) neural network and support vector regression (SVR) models were developed to assess the suitability of groundwater for drinking purposes in the northern Khartoum area, Sudan. The groundwater quality was evaluated by predicting the groundwater quality index (GWQI). GWQI is a statistical model that uses sub-indices and accumulation functions to reduce the dimensionality of groundwater quality data. In the first stage, GWQI was calculated using 11 physiochemical parameters collected from 20 groundwater wells. These parameters include pH, EC, TDS, TH, Cl−, SO4−2, NO3−, Ca+2, Mg+2, Na+, and HCO3−. The primary investigation confirmed that all parameters except for EC and NO3− are beyond the standard limits of the World Health Organization (WHO). The measured GWQI ranged from 21 to 396. As a result, groundwater samples were classified into three classes. The majority of the samples, roughly 75%, projected into the excellent water category; 20% were considered good water and 5% were classified as unsuitable. GWQI models are powerful tools in groundwater quality assessment; however, the computation is lengthy, time-consuming, and often associated with calculation errors. To overcome these limitations, this study applied artificial intelligence (AI) techniques to develop a reliable model for the prediction of GWQI by employing MLP neural network and SVR models. In this stage, the input data were the detected physiochemical parameters, and the output was the computed GWQI. The dataset was divided into two groups with a ratio of 80% to 20% for models training and validation. The predicted (AI) and actual (calculated GWQI) models were compared using four statistical criteria, namely, mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Based on the obtained values of the performance measures, the results revealed the robustness and efficiency of MLP and SVR models in modeling GWQI. Consequently, groundwater quality in the north Khartoum area is evaluated as suitable for human consumption except for BH 18, where highly mineralized water is observed. The developed approach is advantageous in groundwater quality evaluation and is recommended to be incorporated in groundwater quality modeling.
PubDate: 2023-06-01
- Numerical assessment on hydraulic safety of existing conveyance
structurers-
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Abstract: Hydraulic safety assessments on existing conveyance structurers, such as spillway and outlet work, are urgent tasks in water resources management. 3D numerical model has been considered an efficient tool to evaluate hydraulics characteristics of the flow over these works. Flow 3D model involving turbulence and air entrainment modules is used to assess flow rate, velocity, pressure, and air entrainment rate along Ta Trach spillway (Thua Thien Hue, Vietnam). Computed discharges released from spillway and bottom outlet are well matched with analytical results. Besides, two standards of concrete erosion are used to assess hydraulic safety level under different working conditions. In the group of extreme cases including Inflow design flood (IDF) and Exceeded flood (EF) cases, spillway is more likely to damage from cavitation than the culvert with the cavitation index ranging from 0.45 to 1.0. While groups of control gate of spillway and culvert are likely to suffer from abrasion when the high-speed flow appears. The concrete grade of the bottom of culvert is not guaranteed when velocity in culvert is greater than threshold value of 9 m/s, which may cause the surface damage of the construction.
PubDate: 2023-06-01
- Counter-prediction approach to predict the missing values of a spatial
series on the example of the dustiness in the snow cover-
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Abstract: Currently, an increasingly important and complex task is to study and model the impact of human activities on the environment. Such studies are often founded on data from various screenings and monitoring. It is not always possible to get a complete set of data necessary for modeling. The paper proposes an original approach to predict the missing values of a spatial series. As a basis for testing the proposed technique, we used the nonlinear autoregressive neural network. The essence of the approach is that the final forecast of the model is the weighted average result of two forecasts, which are obtained by the model sequentially trained on the values preceding the predicted area on the left and right. Modeling data were obtained by monitoring the dustiness of the snow cover around the copper pit. To test the predictive accuracy of the approach, we created three spatial series. These were the raw series, the mixed series (randomly mixed values of the raw series), and the Gaussian series (independent values drawn from a normal distribution with the same standard deviation and mean as the raw series). The minimum errors were obtained for the original series.
PubDate: 2023-06-01
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