 Subjects -> METEOROLOGY (Total: 106 journals)
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- Quantifying LULC changes in Urmia Lake Basin using machine learning
techniques, intensity analysis and a combined method of cellular automata
(CA) and artificial neural networks (ANN) (CA-ANN)-
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Abstract: Abstract The land use and land cover (LULC) classification accuracy of six machine learning models were compared in Urmia Lake Basin using Landsat images. The overall accuracy confirms that the random forest (RF) (0.957), regularized random forest (RRF) (0.957), the combined method of genetic algorithm and random forest (GA-RF) (0.959) and the combined method of simulated annealing and random forest (SA-RF) (0.957) perform slightly better than the support vector machine (SVM) (0.946) and conditional inference random forest (CIRF) (0.947) though this difference was negligible. The worst classifier was the CIRF with only 43.8% of the grassland pixels correctly assigned to the respective class whereas the GA-RF, SA-RF and RRF performed significantly better with 60.4% of correct classification. Except for the grassland class, the performance of the GA-RF and the SA-RF for the rest of LULC classes were similar (greater than 90%). The magnitude and extent of LULC change was examined using intensity analysis including the interval, category, and transition levels of change. The maximum intensity was from 2006 to 2013, with an annual change in area of 5% which is attributed to the building of the Shahrchay Dam in 2006. The LULC predicted using the combined method of cellular automata (CA) and artificial neural networks (ANN) model (CA-ANN) indicated the soil and rangeland classes are estimated to experience the largest decrease (-5.48%) and increase (7.21%) by year 2035.
PubDate: 2023-11-26
- Lineament extraction and paleostress analysis in the Bikélélé iron
deposit (the Chaillu Massif, Republic of Congo): integration of
ALOS-PALSAR DEM and field investigation data-
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Abstract: Abstract In this study, ALOS-PALSAR DEM has been used along with field investigation data to map geological lineaments and analyze the paleostress in the Bikélélé iron deposit. The methodology involved three approaches: manual extraction of lineaments based on visual interpretation by using four shaded relief images through ArcMap v.10.8 software, and stress inversion and slip tendency methods computed in the Win-Tensor program. The resulting lineament map from manual extraction showed that NW–SE, NE–SW, E–W, and N–S trending are the dominant directions of geological structures. These results were confirmed by field investigation data and were well correlated with previous regional studies. The paleostress reconstruction and analysis indicated the existence of two stress fields that were linked to two deformation phases. The first stress field corresponds to the first deformation phase, D1, which involved NW–SW compression and NE–SW extension. This first deformation, D1, resulted in the formation of NW–SE to N–S sinistral fractures and E–W dextral fractures. This phase also developed N–S to E–W foliations and F1 folds. The second stress field was linked to the second phase of deformation, D2. This latter is characterized by NNE–SSW compression and WNW–ESE extension, that formed N–S dextral fractures, NE–SW to E–W sinistral fractures as well as F2 folds. The deformation phase D2 is associated with the Eburnean orogeny, which affected the Archean basement across the Central Africa. The model summarizing these geological structures and the associated deformation phases has been presented.
PubDate: 2023-11-25
- Modelling storm event-based sediment yield and assessing its heavy metal
loading: case of Lake Victoria's Inner Murchison Bay catchment in Uganda-
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Abstract: Abstract Information on catchment sediment yield and its heavy metal content is crucial to understanding of the transport mechanisms and the potential ecological threats of this sediment. This research aimed at modelling sediment yield and quantifying heavy metals bound to the sediment. The study was conducted in the Lake Victoria's Inner Murchison Bay catchment in Uganda. Depth integrated suspended sediment sampling and discharge measurements were done at the outlets of Nakivubo and Ggaba sub-catchments for ten storm events during a wet season between March and May 2022. The sediment yields for the storms were computed using average suspended sediment concentrations and discharge retrieved from hydrographs. Corresponding event-based sediment yields were modelled using the Modified Universal Soil Loss Equation (MUSLE). MUSLE model was calibrated and validated with observed sediment yields from Nakivubo and Ggaba sub-catchments respectively. Eighteen suspended sediment samples from the two sub-catchments were analysed for contamination by eight heavy metals. Results showed that the mean discharge and Suspened Sediment Concentration (SSC) were: 3.08 ± 1.66 m3/s and 1238 ± 665.6 mg/L; 0.495 ± 0.41 m3/s and 1102 ± 843.7 mg/L for Nakivubo and Ggaba respectively. MUSLE model performance indicators for calibration were: R2 of 0.94, NSE of 0.936 and PBIAS of -7.7 and for validation, R2 of 0.9, NSE of 0.57 and a PBIAS of -15.5. Thus, MUSLE proved a reliable tool for simulating event-wise sediment yield. Cadmium, Lead, and Zinc with contamination factors between 7–11, 1.9–4.1, and 0.9–1.7, respectively were the most prevalent heavy metals from both sub-catchments. Heavy metal pollution exhibited a linear relationship with suspended sediment concentration, with R2 values up to 0.958 and 0.82 in Nakivubo and Ggaba, respectively. The metal pollution in sediment carried into the bay, poses grave ecological and human health risks. Particularly, drinking water and fish sourced from the lake are susceptible to heavy metal contamination. Integrated catchment management practices that reduce sediment and heavy metal transport into Lake Victoria are an urgent requirement.
PubDate: 2023-11-13
- An overview of causal factors in fluctuations of some economic indices in
Iran using impulse response analysis (1990–2022)-
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Abstract: Abstract In recent decades, the Iranian economy has experienced unprecedented financial challenges, resulting in fluctuations in some economic indices. In this study, the impulse response analysis was conducted to identify the causal factors, which are responsible for fluctuating two main indices of gold and land prices during 1990–2022. For this purpose, a vector autoregression model (VAR), with 12 endogenous variables, was constructed, using EViews software. The results revealed that the shock of the inflation rate, market capitalization, and gasoline prices will not significantly fluctuate gold and land prices in Iran. Besides, the results revealed that some variables, such as GDP per capita, stock traded value, the exchange rate, global gold price, and global oil price may fluctuate national gold and land indices in Iran during the observation periods. Among these causal factors, only the shock of exchange rate, with high decomposition variance (> 78%), will immediately fluctuate national gold and land prices. Hence, the co-movement of gold and land price toward the signals of the exchange rate is obvious and could be forecasted for future periods. An important managerial implication is to focus on the controlling approaches of the exchange rate, which is the main driving power of economic fluctuations and instabilities in Iran.
PubDate: 2023-11-10
- Development and application of modeling techniques to estimate the
unsaturated hydraulic conductivity-
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Abstract: Abstract The knowledge of the unsaturated hydraulic conductivity (K) is very essential for the various fields of water resources, irrigation, and hydrology. It is also important to know the phenomena of water movement on the ground constantly. In order to better forecast unsaturated hydraulic conductivity, this article reports the comparison of efficacy of five distinct soft computing approaches: support vector machine (SVM), random forest, Gaussian process (GP), gene expression techniques, and multivariate adaptive regression spline. Three kernels function (Poly, RBF, and PUK) were used in SVM and GP modeling techniques. For fulfill this aim, experimentation has been performed using mini-disc infiltrometer in 20 locations in Ghaggar basin. Total 240 observations were collected, and out of which, 70% were used for training the model and remaining for testing. The input variables of this investigation were sand, clay, silt, bulk density (ρ) and moisture content and output variable was K. The result of modeling techniques suggests that PUK kernel with SVM was superior to the other modeling techniques. This implies that these computational methods can be used to make estimates about the values of K at any time.
PubDate: 2023-11-01
- Modeling the resilient modulus of subgrade soils with a four-parameter
constitutive equation-
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Abstract: Abstract A new constitutive model for the resilient modulus (MR) of subgrade soils was developed in this study based on existing models. The general form of three-parameter model which is common in many constitutive equations for MR was extended here to have four parameters. The proposed model relates MR with the bulk stress, confining pressure and deviatoric stress of the soil using four coefficients. The performance of the model was tested by fitting it to an MR test data obtained from the long-term pavement performance database. A multi-variable nonlinear curve fitting was done using the SciPy library. The results showed that the model has a very good fit to the data with coefficient of determination, root mean square error and mean absolute error values of 0.94, 2.20 and 1.76, respectively. The results of the proposed model were also compared with the bulk stress model and the universal model, which is currently used by the mechanistic-empirical pavement design guide (MEPDG), and obtained to be generally better than both models. The proposed model could potentially be a good alternative to the existing constitutive models if methods for the determination of the k-coefficients could be developed.
PubDate: 2023-11-01
- Instream constructed wetland capacity at controlling phosphorus outflow
under a long‐term nutrient loading scenario: approach using SWAT model-
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Abstract: Abstract At the watershed scale, sustained applications of phosphorus (P) fertilizers on croplands cause unwanted P losses into aquatic systems and subsequent risks of water eutrophication. Nevertheless, instream constructed wetlands (ICWs) offer the possibility to control P transfer from land to water and maintain P concentration in water below levels that negatively affect aquatic life. However, including ICWs in long-term water quality conservation plans is arguable because their long-term functionality is still less known. To better understand this long-term functionality, this study used the soil and water assessment tool (SWAT) model to portend an ICW’s hydrological behavior and its capacity at controlling P release under exceptional climate conditions in the Southeastern Coastal Plain of the United States. Specifically, the model was calibrated and validated for stream and P flow using experimental ICW data and an assumption of a continuous corn-soybean rotation across an agricultural watershed. A multi-decadal simulation was used to evaluate monthly balances of dissolved P (ΔDP) and total P (ΔTP) under a variable climate spectrum and a continuous nutrient loading scenario. Analyses of monthly ΔDP and ΔTP time series over consecutive decadal periods 2001–2010 and 2011–2020 showed signals of negative P balances at a probability of 0.18. Point biserial correlations analyses unveiled a significant relationship between monthly ICW’s P balances and precipitation variability at the watershed scale. The P releases were under control during low to moderate precipitation conditions, but extreme precipitation events caused abnormal P outflows. Hence, ICWs could be a sustainable option for long-term P outflow control under low to moderate hydrologic regimes.
PubDate: 2023-11-01
- Spatiotemporal trend analysis of groundwater level changes, rainfall, and
runoff generated over the Notwane Catchment in Botswana between 2009 and
2019-
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Abstract: Abstract The semi-arid south-eastern part of Botswana has recently been experiencing severe water shortages, and the demand currently surpasses the supply in the greater Gaborone area. Within the context of increased stormwater runoff generated over the area and the potential for groundwater recharge, this study aims to investigate the relationships between groundwater depths and rainfall amounts and identify their patterns and significance or lack thereof over Botswana’s largest water demand centre that falls within the data-scarce Notwane catchment area (NCA). Trend analysis of monthly rainfall and groundwater levels between 2012 and 2019 and their homogeneity were undertaken using the Mann-Kendal test, followed by the application of the water balance method to estimate runoff over the catchment between 2009 and 2019. Runoff and precipitation between the two periods were compared using paired t-tests. Investigations revealed that rainfall increased insignificantly, whereas groundwater depth generally increased significantly. The homogeneity test revealed a general insignificant increase in rainfall over NCA. No catchment-wide conclusions were obtained regarding groundwater depth increases. Water-balance computed runoff in 2019 was an increase of 13.7% from that computed in 2009, despite the conservative 3% increase in rainfall between the two periods. Increase in runoff could even be higher if land use changes were incorporated. This study revealed that there is groundwater recharge over the catchment, particularly after heavy rainfall events. The results of this study offer insights for identifying groundwater recharge potential zones, which could inform decision making with regard to strategies for induced groundwater recharge to replenish groundwater resources that can conjunctively be used with surface water resources.
PubDate: 2023-11-01
- Integrating 2D hydrodynamic, SWAT, GIS and satellite remote sensing models
in open channel design to control flooding within road service areas in
the Odaw river basin of Accra, Ghana-
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Abstract: Abstract Infrastructure network security and resiliency have become major problems in recent years as a result of an increasing number of catastrophic weather-related occurrences around the world. Major catastrophic events in Accra within the Odaw river basin began in 1959, with the flood claiming the lives of roughly 154 Ghanaians and causing varying levels of burns and injuries. The Odaw river basin has a total catchment area estimated to be about 275 km2 which drains the major urbanised areas of Accra including Ga East, Ga west, Accra metropolitan assembly and Adenta Municipal Assembly further upstream. These areas mostly get affected by flash floods. This study used integrated 2D hydrodynamic, SWAT, Geographic information system (GIS) and Remote Sensing (RS) models to estimate flood depth and the extent to map out inundated areas for effective open channel design to control flooding within the Odaw river basin. Landsat images were classified using a Random Forest algorithm to produce a LULC map for the SWAT model. SWAT model was used to delineate the basin with its channels, sub-basins, outlet points, flow length, area and runoff depth of the Odaw river basin using GIS. A 2D hydrodynamic model was used to estimate the flood depth (0–0.3 m, 0.3–5.0 m and > 5.0 m) and extent within the Odaw river basin using HER-RAS 6.0 software. Flood depth greater than 0.3 m was identified to be dangerous because it causes vehicles to float and submerge. George Bush Motorway, Kwame Nkrumah Motorway, Tetteh Quarshie Interchange, Obetsebi Lamptey Circle, Graphic Road, Black Meteors Lane, Ring Road Central, Afram Road, Guggisberg Avenue and Hall Street were identified road networks within this flood depth (> 0.3 m). Achimota, Asofan, Ashouman, Akokome, Adenkrebi, Kweman, Kokomlemle, Alajo and Tesano were also road service areas identified within this flood depth greater than 0.3 m. A 50-year peak flow of 447.801 m3/s which occurred in 2016 within the basin was obtained from field measurement by Edward in The Kwame Nkrumah University of Science and Technology. This peak flow was used to propose an open channel design to control flooding within the basin using Manning’s equation. The results of the study show that the integrated 2D hydrodynamic, SWAT, GIS and RS models have better performance in open channel design.
PubDate: 2023-11-01
- SDIQR mathematical modelling for COVID-19 of Odisha associated with influx
of migrants based on Laplace Adomian decomposition technique-
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Abstract: Abstract In this study, the Laplace Adomian decomposition technique (LADT) is employed to analyse a numerical study with the SDIQR mathematical model of COVID-19 for infected migrants in Odisha. The analytical power series and LADT are applied to the Covid-19 model to estimate the solution profiles of the dynamical variables. We proposed a mathematical model that incorporates both the resistive class and the quarantine class of COVID-19. We also introduce a procedure to evaluate and control the infectious disease of COVID-19 through the SDIQR pandemic model. Five compartments like susceptible ( \(S\) ), diagnosed ( \(D\) ), infected ( \(I\) ), quarantined ( \(Q\) ) and recovered ( \(R\) ) population are found in our model. The model can only be solved approximately rather than analytically as it contains a system of nonlinear differential equations with reaction rates. To demonstrate and validate our model, the numerical simulations for infected migrants are plotted with suitable parameters.
PubDate: 2023-11-01
- Modelling spatial–temporal expansion of Lilongwe City using Shannon’s
entropy model through semi-dynamic environmental mapping and analysis-
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Abstract: Abstract This study involves analysis of the urban spatial–temporal expansion of Lilongwe City from 1973 to 2020 using Shannon’s entropy model through time-series satellite mapping. Landsat images from 1973 to 2020 in a nearly 10-year interval are used to determine spatial–temporal land use/cover changes. The city is zoned into 1 km concentric rings and four pie sections to determine both directional and spatial urban expansion trends while Shannon’s entropy model is employed to determine the degree of dispersity of the city’s sprawl. A linear regression model incorporating population as an explanatory variable is then applied to predict the spatial expansion trends for Lilongwe City. Results show that the built-up area in Lilongwe City expanded by 465.4% (9.9% per year) from 1973 to 2020, making it the second-largest land use/cover type in the city, after vegetation. Consequently, vegetation cover decreased by − 32.7% (− 0.7% per year) during the same period. High relative entropy indices (> 0.9) obtained from Shannon’s entropy model indicate a dispersed urban development for the city during the entire period of study. The North-West quadrant of the city has the highest proportion of urban expansion while the North-East quadrant has the lowest proportion, in a relative sense. Regression model predictions show that the city will most likely continue to expand by the year 2023 and then increase exponentially by the year 2033, due to high population growth. The results of this study will assist city authorities to control the expansion of the city and anticipate patterns for future urban sprawl.
PubDate: 2023-11-01
- Modeling and analysis of novel COVID-19 outbreak under fractal-fractional
derivative in Caputo sense with power-law: a case study of Pakistan-
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Abstract: Abstract In this paper, a five-compartment model is used to explore the dynamics of the COVID-19 pandemic, taking the vaccination campaign into account. The present model consists of five components that lead to a system of five ordinary differential equations. In this paper, we examined the disease from the perspective of a fractal fractional derivative in the Caputo sense with a power law type kernal. The model is also fitted with real data for Pakistan between June 1, 2020, and March 8, 2021. The fundamental mathematical characteristics of the model have been investigated thoroughly. We have calculated the equilibrium points and the reproduction number for the model and obtained the feasible region for the system. The existence and stability criteria of the model have been validated using the Banach fixed point theory and the Picard successive approximation technique. Furthermore, we have conducted stability analysis for both the disease-free and endemic equilibrium states. On the basis of sensitivity analysis and the dynamics of the threshold parameter, we have estimated the effectiveness of vaccination and identified potential control strategies for the disease using the proposed model outbreaks. The stability of the concerned solution in Ulam-Hyers and Ulam-Hyers-Rassias sense is also investigated. For the proposed problem, some results regarding basic reproduction numbers and stability analysis for various parameters are represented graphically. Matlab software is used for numerical illustrations. Graphical representations are given for different fractional orders and for various parametric values.
PubDate: 2023-11-01
- Inverse distance weighted (IDW) and kriging approaches integrated with
linear single and multi-regression models to assess particular
physico-consolidation soil properties for Kirkuk city-
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Abstract: Abstract Due to significant budgetary constraints, it is impractical to experimentally study a wide territory to identify soil characteristics over the entire city. Thus, the focus of this work was on employing a geographic information system, as indicated by both Kriging and inverse distance weighted (IDW) approaches, incorporated with linear single and multi-regression models to include a dataset for 56 different soil samples from various sites of Kirkuk city. The IDW and Kriging techniques were utilized to study the physical and consolidation soil parameters such as the percentages of clay, silt, sand, and gravel, preconsolidation pressure (Pc), vertical effective stress (Po), compression index (Cc), and recompression index (Cr). The physico-consolidation relationship was assessed using a linear single regression model in which each consolidation and physical soil property were individually associated. Furthermore, the physico-consolidation association has been determined using a linear multi-regression model where each consolidation soil characteristic was correlated with all the inspected physical soil properties. This study’s findings focused on developing two sets of geographic digital maps for examining different physical and consolidation characteristics of Kirkuk soils. Based on the physico-consolidation correlation analysis, Cc and all physical soil characteristics have substantial positive associations. The Pc and Po values were accurately predicted by the suggested linear multi-regression model. Combined with the presented statistical models, the resulting digital soil maps can provide comprehensive geographical, mechanical, and agricultural representations of soil composition and morphology in Kirkuk city.
PubDate: 2023-11-01
- Selection of spatial prediction models of saturated hydraulic conductivity
in soils containing rock fragments in an Andean micro-basin-
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Abstract: Abstract Research has been carried out to evaluate the effects of rock fragments on the hydraulic conductivity of soils; however, spatial relationships have not been established in analyses of such effects. In the present investigation, the properties of the soil were determined, as well as the proportion of the different fractions of rock fragments, and then computational modeling was used to develop a model that would allow establishing the spatial relationship of the soil saturated hydraulic conductivity and the rock fragment contents, organic carbon, clay, horizon thickness and total porosity of soil horizons up to 1 m depth in an Andean basin. An iterative spatial 3D weight matrix and hydrologic response units were used to generate the neighborhood pattern. The results made it possible to show spatially that the increases in total porosity and in the content of rock fragments increased the response variable under certain considerations in inversion of the distance matrix, that is, the soil saturated hydraulic conductivity; instead, the increases in the thickness of the horizons and the organic carbon and clay contents decreased the response variable. The spatial model with the best fit was presented when the spatial weight matrix power factor b was equal to 0.39. The model could be used under its constraints for an efficient and economic estimation of the saturated flow of soil water, because the predictor variables are easier to determine and less expensive, and the calculation can be performed at different depths of the soil profile.
PubDate: 2023-11-01
- Simulating daily sediment transport using the Water Quality and Sediment
Model (WQSED)-
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Abstract: Introduction High erosion and sediment yield rates continue to pose a significant threat to the environment globally. Information on erosion and sediment rates is key for supporting effective and sustainable mitigation measures. Models that estimate sediment yield are vital in providing information about erosion and sediment yield rates, as empirical studies are prohibitive over large spatial and temporal scales. Methods In this study, we simulate daily sediment transport using the WQSED model and assess the effectiveness of the tool in providing crucial estimations of sediment yield. The model structure links the Modified Universal Soil Loss Equation (MUSLE) to a simple sediment storage component. The model was applied to the Odzi River catchment in Zimbabwe and The Rio Tanama River catchment in Puerto Rico, where daily observations of sediment yield exceeding a decade were available for calibration and validation. Results In both catchments, we achieved a coefficient of efficiency and R2 and NSE of > 0.7 during model calibration and > 0.6 during model validation. The percentage bias remained below 45% for both calibration and validation periods. Conclusion These results indicate that the WQSED model can be applied to provide estimates of sediment yield that are reliable for erosion, sediment yield and water quality management. An effective and relatively simple sediment yield model incorporating sediment storage is essential for catchment management in erosion-prone areas.
PubDate: 2023-11-01
- Seismic modelling of the Upper Cretaceous, Khalda oil field, Shushan
Basin, Western Desert, Egypt-
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Abstract: Abstract Khalda oil field lies in the North of the Western desert of Egypt. In the present study, 2D seismic dataset is used to interpret and map main structures and their trends. The main aim of this work is to create numerous depth structural maps throughout the whole reservoir package in the Khalda oil field which composes of four main horizons which are Abo Roash (F), Abo Roash (G), Upper Bahariya, and Lower Bahariya Members and build a 3D geological model. The four interfaces were detected and picked and interpreted using a 2D seismic dataset, and the velocity model was used to convert the two-way time structural maps into depth domain. The 3D structural model of both Upper and Lower Bahariya units and structural cross section extracted from the 3D structural model to show the lateral extension of reservoir zones and their thickness variation including faults. The results show that the structural maps and a 3D geological model were successfully established to include the fault framework model. Throughout the Late Cretaceous to Eocene epochs, the reservoir package encountered both extensional and contractional regimes, compartmentalized by dip-slip faults with NW–SE orientation. The various structural maps, a 3D model, and extracted arbitrary section provide a detailed subsurface description for understanding major fault mechanisms. The main structural form and the lateral extension of reservoir zones and their thickness variation in the arbitrary section, 3D model, and TWT and depth structural contour maps could show 27 dip-faults that are primarily normal faults with dominated NW–SE and WNW–ESE trends in the Late Albian (Lower Bahariya), Early Cenomanian (Upper Bahariya), and Early Turonian (Abo Roash (F) and (G)) members, representing a great framework for planning appropriate drilling campaigns for Khalda oil field exploration and development.
PubDate: 2023-11-01
- A hybrid deep learning model for rainfall in the wetlands of southern Iraq
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Abstract: Abstract Machine learning is being used by researchers and computer scientists to develop a new method for predicting rainfall. Due to the non-linear relationship between input data and output conditions, rainfall prediction is hard, so deep neural network (DNN) models substitute for costly, complex systems. Deep neural network-based weather forecasting models can be designed quickly and cheaply to predict rainfall. On the other hand, water levels depend on rainfall. Unpredictable rainfall due to climate change might cause floods or droughts. Many individuals, especially farmers, rely on rain forecasts. In our study, we focus on the area of marshes in southern Iraq, some of the most famous landmarks in the area (and the world), where the Shatt al-Arab flows into the Arabic Gulf and the Tigris and Euphrates rivers developed within the Mesopotamian plain to create a natural balance. Since the beginning of the 1980s, the wetlands, sometimes known as "the marshes," have experienced droughts. And by the late 1990s, a sizable portion of the marshes had dried up, leaving the arid and salty Sabkha lands void of life, particularly lands with vast bodies of water and high levels of human activity. Moreover, the corresponding regions have undergone visible hydrological and climatic changes. In this study focuses on the marshes of southern Iraq and aims to develop a rainfall forecasting model. We propose a novel approach based on optimized LSTM and hybrid deep learning algorithms to improve the forecasting of average monthly rainfall. To evaluate the efficiency of the predictions, a comparison of the predicted rainfall and the actual recorded rainfall is made, and the performance and accuracy of the models are examined. The hybrid convolutional stacked bidirectional long-short term memory (CNN-BDLSTMs) outperformed the other models.
PubDate: 2023-11-01
- Runoff modeling using SCS-CN and GIS approach in the Tayiba Valley Basin,
Abu Zenima area, South-west Sinai, Egypt-
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Abstract: Abstract Water is an essential resource that is required in every element of life. Management of water resources necessitates adequate information about the amounts of water supplied from the basins that flows into the sea in particular during the flood seasons. Moreover, because of global climate changes, the hydrologic modeling of the catchments is critically essential for the socioeconomics and life modalities in the study area. So, it's critical to estimate runoff at the watershed level for better understanding of hydrologic processes and locating hotspot areas. Rainfall and runoff are the main hydrologic factors in the evaluation of water resources and they are also the most significant components regulating the basin's groundwater recharge. For this reason, using hydrologic models with trustworthy data is necessary to simulate and estimate water availability with using SCS-CN and GIS approach. In fact, there are a variety of methods available to estimate runoff from rainfall, but the SCS-CN model continues to be the most well known and often used method because runoff curve number (CN) is a fundamental component of the SCS-CN method. In this scope, the purpose of this study is to perform runoff modeling using SCS-CN and GIS approach in the Tayiba Valley Basin. In addition, the SCS-CN model employed inputs of hydrologic soil group (HSG), land use, antecedent moisture condition (AMC), and rainfall value to create CN and compute the CNw and they are necessary inputs to the SCS-CN model for estimating runoff in the Tayiba Valley Basin. Within this context, the use of geographic information system gives us the ability to map and interpret spatial data for factors that affect runoff, such as land use, HSG, digital elevation model, and rainfall. So, results showed the daily rainfall from the Tayiba Valley Basin for nineteen years, i.e., 2001 to 2019. As a result, the annual average surface runoff calculated for the Tayiba Valley Basin is 8.62 mm and the total average volume of runoff is 3080.02 m2. Finally, these results demonstrate that the hydrologic model will effectively support the Tayiba Valley Basin integrated management through evaluating runoff. In addition, the outcome may also assist many decision-makers in developing and putting into practice effective intervention techniques in the study area.
PubDate: 2023-11-01
- Delineation of groundwater potential zones using the AHP technique: a case
study of Alipurduar district, West Bengal-
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Abstract: Abstract Increasing population with increasing demand of groundwater affects the level of groundwater. In the context of considerable change in the use of groundwater pattern, particularly with continuous increase in demand for groundwater due to many reasons, the present paper attempts to delineate groundwater potential zones (GWPZ) using integrated remote sensing, geographic information systems (GIS) and analytic hierarchy process (AHP) methods. To transform and harmonize geographic data and weightage ranking to get reliable information, geographic information systems are combined with analytical hierarchical processes. The current study has been done in the district where many areas are under tea garden and cultivated land. The use of excess of groundwater results in a drop in the water level. The mapping and the identification of groundwater potential zones were done for the Ganga alluvial plain of Alipurduar District of India. The groundwater potential index (GPI) was computed based on several factors (e.g., land use–land cover, soil type, geology, elevation, slope, rainfall, normalized difference vegetation index, drainage density, pre- and post-monsoon groundwater depth, etc.). To generate the groundwater potential zone map of the study area, an overlay weighted sum method was applied to integrate all thematic criteria. Groundwater potential index maps have been classified into five zones. The excellent potential zone comprise 50.5% (1583.68 km2), good 27.4% (859.26 km2), moderate11.3% (354.37 km2), poor 7.1% (222.66 km2) and very poor 3.7% (116.03 km2), respectively. After that, the maps were verified with groundwater-level fluctuation data of 30 observed wells through the ROC (receivers operating characteristic) curve. This paper has important implications for planning the sustainable groundwater plan and also different purposes, such as natural and artificial recharge, watershed delineation and proper water usage, can be effectively implemented in this agriculture-dominated areas in the district.
PubDate: 2023-11-01
- Prediction and mapping of land degradation in the Batanghari watershed,
Sumatra, Indonesia: utilizing multi-source geospatial data and machine
learning modeling techniques-
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Abstract: Abstract In the present study, the Geospatial Artificial Intelligence (Geo-AI) is proposed to overcome challenges and phenomena related to land degradation identification in the field by integrating multi-source geospatial data and machine learning modeling techniques. The study area is located in the Batanghari watershed, Sumatra, Indonesia, which is a tropical environment. The existence of environmental problems in the study area can be identified based on land degradation. This study’s novelty model is that it is the first to integrate the six main variables of multi-source geospatial data—topographical, biophysical, bioclimatic, geo-environmental, global human modification, and accessibility—in predicting and mapping the potential of land degradation in Indonesia’s tropical environment. Support Vector Machine (SVM), Minimum Distance (MD), Classification and Regression Trees (CART), Gradient Tree Boost (GTB), Naïve Bayes (NB), Random Forest (RF) are machine learning modeling algorithms used to predic and map land degradation in the study area. The prediction results from these modeling algorithms can be compared and evaluated to get the most optimal performance and accuracy. During the modeling phase, 70% of the reference data were divided into training and 30% into validation. The performance of the model was compared using three accuracy assessment processes (producer accuracy, user accuracy, and overall accuracy). The overall accuracy of the results of the comparison and evaluation of machine learning modeling on the SVM, MD, CART, GTB, NB, and RF algorithms in the study area are 52.8, 34.5, 81.2, 85.8, 36.3, and 86.2%, respectively. Therefore, the study concluded that the RF, CART, and GTB are machine learning modeling algorithms that were proposed to be applied and to produce a land degradation map in the study area. The results of this study can be used by policymakers in making decisions related to environmental management, ecosystem rehabilitation, and restoration. Technically, this can also be applied to other watersheds with similar characteristics. In addition, the results of this study can also be used to develop an intelligent watershed monitoring system to effectively monitor land cover change.
PubDate: 2023-11-01
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