Evaluation of recent advanced soft computing techniques for gully erosion susceptibility mapping: A comparative study

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion

Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco

Gully erosion has been identified in recent decades as a global threat to people and property. This problem also affects the socioeconomic stability of societies and therefore limits their sustainable development, as it impacts a nonrenewable resource on a human scale, namely, soil. The focus of this study is to evaluate the prediction performance of four machine learning (ML) models: Logistic Regression (LR), classification and regression tree (CART), Linear Discriminate Analysis (LDA), and the k-Nearest Neighbors (kNN), which are novel approaches in gully erosion modeling research, particularly in semi-arid regions with a mountainous character. 204 samples of erosion areas and 204 samples of non-erosion areas were collected through field surveys and high-resolution satellite images, and 17 significant factors were considered. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient. kNN is the ideal model for this study. The value of the AUC from ROC considering the testing datasets of KNN is 0.93; the remaining models are associated to ideal AUC and are similar to kNN in terms of values. The AUC values from ROC of GLM, LDA, and CART for testing datasets are 0.90, 0.91, and 0.84, respectively. The value of accuracy considering the validation datasets of LDA, CART, KNN, and GLM are 0.85, 0.82, 0.89, 0.84 respectively. The values of Kappa of LDA, CART, and GLM for testing datasets are 0.70, 0.65, and 0.68, respectively. ML models, in particular KNN, GLM, and LDA, have achieved outstanding results in terms of creating soil erosion susceptibility maps. The maps created with the most reliable models could be a useful tool for sustainable management, watershed conservation and prevention of soil and water losses.info:eu-repo/semantics/publishedVersio

Predicting Water Availability in the Antarctic Dry Valleys using Geographic Information Systems and Remote Sensing

Water is one of the most important ingredients for life on Earth. The presence or absence of biologically available water determines whether or not life will exist. Antarctica is an environment where abiotic constraints, particularly water, strongly influence the distribution and diversity of biota. As Antarctic biology is relatively simple when compared to more temperate climates, it is a prime location for researching constraints on biodiversity, and what may be the impacts of changes to these constraints resulting from climate change and human disturbance. This research uses Geographic Information Systems (GIS) and remote sensing to develop a relative water availability index of three Dry Valleys in Southern Victoria Land, Antarctica. This study area is being used for the IPY Terrestrial Biocomplexity project, an international collaboration researching the distribution, diversity and complexity of biology in the Dry Valleys. The development of a predictive water availability model will contribute greatly to their research goals. This thesis describes the sources of biologically available water in the Dry Valleys and its interaction with biota. Remotely sensed data of these sources is gathered and various methods of analysing the data are explored. This includes creating a mean snow cover distribution model from MODIS data over 4 summer seasons, and Landsat7 ETM+ surface temperature data. These data sets, combined with a high resolution LIDAR Digital Elevation Model and glacier and lake locations, are then analysed with GIS to produce a Compound Topographic Index (CTI), a model showing the likely accumulation and dispersal of liquid water given the spatial distribution of water sources and the flow of water over the terrain according to the influence of gravity. Visualisation techniques are used to validate the resulting model, including the use of 3D visualisation and comparison of drainage patterns using overlays of a high resolution ALOS image. This research concludes that GIS and remote sensing are valuable tools for predicting water distribution in Antarctica. Although cloud cover, varied illumination and differing spatial resolutions can create limitations, remote sensing's cost effective and environmentally sound method of data capture and the computational and spatial modelling capabilities of GIS make their use well suited to the Antarctic environment

Assessment Of Climate Change And Agricultural Land Use Change On Streamflow Input To Devils Lake: A Case Study Of The Mauvais Coulee Sub-Basin

Since 1993, Devils Lake in North Dakota has experienced a prolonged rise in lake level and flooding of the lake’s neighboring areas within the closed basin system. Understanding the relative contribution of climate change and land use change is needed to explain the historical rise in lake level, and to evaluate the potential impact of anthropogenic climate change upon future lake conditions and management. Four methodologies were considered to examine the relative contribution of climatic and human landscape drivers to streamflow variations: statistical, ecohydrologic, physically-based modeling, and elasticity of streamflow; for this study, ecohydrologic and climate elasticity were selected. Agricultural statistics determined that Towner and Ramsey counties underwent a crop conversion from small grains to row crops within the last 30 years. Through the Topographic Wetness Index (TWI), a 10 meter resolution DEM confirmed the presence of innumerable wetland depressions within the non-contributing area of the Mauvais Coulee Sub-basin. Although the ecohydrologic and climate elasticity methodologies are the most commonly used in literature, they make assumptions that are not applicable to basin conditions. A modified and more informed approach to the use of these methods was applied to account for these unique sub-basin characteristics. Ultimately, hydroclimatic variability was determined as the largest driver to streamflow variation in Mauvais Coulee and Devils Lake

High groundwater in irrigated regions: model development for assessing causes, identifying solutions, and exploring system dynamics

2021 Spring.Includes bibliographical references.Waterlogging occurs in irrigated areas around the world due to over-irrigation and lack of adequate natural or artificial drainage. This phenomenon can lead to adverse social, physical, economic, and environmental issues, such as: damage to crops and overall land productivity; soil salinization; and damage to homes and building foundations. Solutions to waterlogging include implementation of high-efficient irrigation practices, installation of artificial drainage systems, and increased groundwater pumping to lower the water table. However, in regions governed by strict water law, wherein groundwater pumping is constrained by impact on nearby surface water bodies, these practices can be challenging to implement. In addition, current engineering and modeling approaches used to quantify soil-groundwater and groundwater-surface water interactions are crude, perhaps leading to erroneous results. An accurate representation of groundwater state variables, groundwater sources and sinks, and plant-soil-water interaction is needed at the regional scale to assist with groundwater management issues. This dissertation enhances understanding of major hydrological processes and trade-offs in waterlogged agricultural areas, through the use of numerical modeling strategies. This is accomplished by developing numerical modeling tools to: (1) analyze and quantify the cause of high groundwater levels in highly managed, irrigated stream-aquifer systems; (2) assess the impact of artificial recharge ponds on groundwater levels, groundwater-surface water interactions, and stream depletions in irrigated stream-aquifer systems; (3) and gain a better understanding of plant-soil-water dynamics in irrigated areas with high water tables. These objectives use a combination of agroecosystem (DayCent) and groundwater flow (MODFLOW) models, sensitivity analysis, and management scenario analysis. Each of these sub-objectives is applied to the Gilcrest/LaSalle agricultural region within the South Platte River Basin in northeast Colorado, a region subject to high groundwater levels and associated waterlogging and infrastructure damage in the last 7 years. This region is also subject to strict water law, which constrains groundwater pumping due to the effect on the water rights of the nearby South Platte River. Results indicate that recharge from surface water irrigation, canal seepage, and groundwater pumping have the strongest influence on water table elevation, whereas precipitation recharge and recharge from groundwater irrigation have small influences from 1950 to 2012. Mitigation strategy implementation scenarios show that limiting canal seepage and transitioning > 50% of cultivated fields from surface water irrigation to groundwater irrigation can decrease the water table elevation by 1.5 m to 3 m over a 5-year period. Decreasing seepage from recharge ponds has a similar effect, decreasing water table elevation in local areas by up to 2.3 m. However, these decreases in water table elevation, while solving the problem of high groundwater levels for residential areas and cultivated fields, results in a decrease in groundwater discharge to the South Platte River. As the intent of the recharge ponds is to increase groundwater discharge and thereby offset stream depletions caused by groundwater pumping, mitigating high water table issues in the region can be achieved only by (1) modifying fluxes of sources and sinks of groundwater besides recharge pond seepage, or (2) modifying or relaxing the adjudication of water law, which dictates the need for offsetting pumping-induced stream depletion, in this region. The modeling tools developed in this dissertation, specifically the loose and tight coupling between DayCent and MODFLOW, can be used in the study region to quantify pumping-induced stream depletion, recharge pond induced stream accretion, and the interplay between them in space and time. In addition, these models can be used in other irrigated stream-aquifer systems to assess baseline conditions and explore possible effects of water management strategies

Using Spatial Data for Geo-Environmental Studies

The physically-based spatially-distributed model PROMET (Processes of Radiation, Mass and Energy Transfer) is applied to the Greater Damascus Basin, which is considered as one of the most important basins in Syria, to serve as a case study of using spatial data for Geo-environmental studies. Like most areas of the Middle East, the study area is characterized by large temporal and spatial variations in precipitation and by limited water resources. Due to the increasing water demand caused by the economic development and the rapid growth of population, the study area is expected to suffer from further water shortages in the future. This highlights the necessity of developing an integrated Decision Support System (DSS) to evaluate strategies for efficient and sustainable water resources management in the basin, taking into consideration global environmental changes and socio-economic conditions. The work presented here represents the first steps toward achieving this goal through applying a distributed hydrological model (an important component of any integrated DSS for water resources management) to the Greater Damascus Basin utilizing different types of spatial data used as time-dependent (e.g., meteorology) and time-independent (e.g., topography and soil) input parameters. The model PROMET, which was developed within the GLOWA-Danube project as part of the decision support system DANUBIA, is run on an hourly time step (for the period from 1991 to 2005) and a 180*180m spatial resolution to simulate the water and energy fluxes in this basin. The model is embedded within a raster-based GIS-structure which facilitates the integration of the diverse types of spatial data. The spatial information related to topography (such as elevation, slope, and exposition) as well as those related to runoff routing (such as upstream-area, channel width, and downstream proxel) are automatically extracted from Digital Elevation Model (Shuttle Radar Topography Mission, SRTM-90m DEM). The spatial patterns of the different land use/land cover classes are derived from remote sensing data (classification of a cloud-free LANDSAT 7 ETM+ image using the supervised classification algorithm). The spatial fields of meteorological input data are provided on an hourly basis through spatiotemporal interpolation of the measurements of the available weather stations. Spatial information about the soil texture is provided through generalization and aggregation of the soil type classes of the Soil Map of Syria (prepared by USAID) and transferring the soil types to texture classes. Several pedotransfer functions are then used to estimate the soil hydraulic properties for each soil texture class (and each soil layer) found in the study area. While plant physiological parameters (which are assumed to be static, such as minimum stomatal resistance) are estimated for each vegetation class using information taken from literature sources, the temporal evolution of Albedo and Leaf Area Index (LAI) are derived from five cloud-free LANDSAT-7 images acquired at different seasons of the year. The goodness of the results obtained by the model PROMET are verified and/or validated by comparing them either with their corresponding data observed in the filed or with remote sensing-derived information (e.g., snow cover). Two subcatchments are selected for the purpose of calculating the spatially-distributed annual water balances. The results indicate that the modelled mean annual runoff volume fits well with the measured discharge for both chosen subcatchment. In addition, the simulated discharge is compared to the observed one (at seven gauge stations) on a monthly basis, covering the whole simulation period (15 years). The results of the regression analysis for each of these gauge stations (with slope of regression line ranges from 0.79 to 1.04; coefficient of determination 0.69-0.90; and Nash-Sutcliffe Coefficient 0.73-0.95) indicate that there is a good correlation between simulated and observed monthly mean discharge volumes