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Journal ArticleDOI

Gully erosion and climate induced chemical weathering for vulnerability assessment in sub-tropical environment

TL;DR: In this paper, the authors present a novel technique of gully erosion susceptibility mapping by employing EBO (Eco-geography based optimization) with its ensembles: Bagging, Dagging, and Decorate.
About: This article is published in Geomorphology.The article was published on 2022-02-01 and is currently open access. It has received 14 citations till now. The article focuses on the topics: Erosion & Weathering.
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Journal ArticleDOI
TL;DR: In this paper , the impact of climate change and its associated extreme rainfall event is the major threats of land resources in subtropical monsoon dominated region, and the authors have considered the Support Vector Machine (SVM), Analytical Neural Network (ANN) and Deep Learning Neural Networks (DLNN) for the estimation of gully erosion susceptibility in Dwarkeswar River basin.
Abstract: Abstract The impact of climate change and its associated extreme rainfall event is the major threats of land resources in subtropical monsoon dominated region. In this study, we have considered the Support Vector Machine (SVM), Analytical Neural Network (ANN) and Deep Learning Neural Network (DLNN) for the estimation of gully erosion susceptibility in Dwarkeswar River basin. The ensemble Global circulation Model (GCM) has been considered for simulating the rainfall scenario in the projected period, i.e. 2100s. All selected parameters' VIF (variance inflation factor) and TOL (tolerance) ranges are 1.05 to 11.56 and 0.20 to 0.95, respectively. In the training dataset, AUC values of SVM, ANN and DLNN are 0.95, 0.95 and 0.96, respectively. In the case of validation, AUC values are .86, 0.87 and 0.90, respectively. Here, the DLNN is the most optimal model in terms of the predictive capacity. The AUC (Area Under Curve) values from ROC (Receiver operating characteristics Curve) of the DLNN model for training and validation datasets are 0.96 and 0.90, respectively. The values of sensitivity, specificity, PPV and NPV in the case of validation datasets in DLNN are 0.88, 0.76, 0.80 and 0.79, respectively. There is an increasing tendency of rainfall and gully erosion susceptibility in the projected period. This type of information is helpful to the decision maker in this respective region to implement the long-term planning for escaping this type of situation.

7 citations

Journal ArticleDOI
TL;DR: In this article , the authors analyzed the morphometric anomalies of the lateritic badlands (West Bengal, India) to understand the erosion intensity, triggering factors and landscape evolution using the ALOS AW3D30 DEM and quantitative techniques of various hydro-geomorphic processes (viz., basin morphometry, rainfall runoff simulation, geomorphic threshold, erosion indices, sediment yield, SPI, and STI etc.).
Abstract: Abstract The present geomorphic study analyzes the morphometric anomalies of the lateritic badlands (West Bengal, India) to understand the erosion intensity, triggering factors and landscape evolution using the ALOS AW3D30 DEM and quantitative techniques of various hydro-geomorphic processes (viz., basin morphometry, rainfall-runoff simulation, geomorphic threshold, erosion indices, sediment yield, SPI, and STI etc.). The GIS-based analysis reflects that there is the requitement of minimum drainage area (i.e. 55.71–349.01 m2) to maintain one metre of gully channel in the basins which have the network density of 2.86–13.76 km km−2. The critical slope (i.e. geomorphic threshold) of badland terrain varies from 0.0222 to 0.0407 metre metre−1, having dominance of overland flow erosion to initiate gully heads. The basin-wise SCS-CN and USLE coupling estimates that within the daily rainfall range of 11.41–66.41 mm day−1, the potential sediment yield of gully basins varies from 2.15 to 9.10 t ha−1. The S L index (i.e. 0.81–47.32 m) diagram reflects the beats of gully energy profile due to stream erosion enhancement, slope steepness, resistance of underlying lithologies and active tectonics. Hypsometric integral values of the gully basins, ranging from 0.4 to 0.6, emphasize a low entropy-based fluvial system and development of mature-youthful landform stage. The evolutionary stages of badlands are explained here by the connectivity model and hillslope – gully – river coupling system, showing a quasi-equilibrium to complex stage of landscape development at present.

2 citations

Journal ArticleDOI
TL;DR: In this article , the potential impact of climate and land use and land cover (LULC) change on soil erosion has been estimated, in which the future rainfall scenario in the projected period and LULC dynamics in the same period have been estimated.
Abstract: In the present study, the potential impact of “climate” and “land use and land cover (LULC)” change on soil erosion has been estimated. In this perspective, the future rainfall scenario in the projected period and LULC dynamics in the same period has been estimated. The pattern, intensity and amount of rainfall are always changing which has been established by different researchers. The “average annual soil erosion” for the base period and projected period has been estimated with considering “Revised Universal Soil Loss Equation (RUSLE)”. The “cellular automata (CA”) and “Markov chain (MC)” model has been used for projecting the future LULC in projected period. The selection of the suitable “general circulation model (GCM)” model has been done with the help of statistical analysis. That is very confusing task in any type of climatic modelling with considering suitable “GCM”. Then the combine impact of changing climate and “LULC” has been considered for future soil erosion modelling. There is a rising pattern of “soil erosion” from base year to projected period has been confirmed in this research. This type of information is useful to the regional planner to take the suitable remedies in keeping in the view of sustainable and long term planning.

1 citations

References
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Journal ArticleDOI
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

17,017 citations

Journal ArticleDOI
01 Aug 1996
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Abstract: Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.

16,118 citations

Journal ArticleDOI
18 Jun 1982-Nature
TL;DR: The early Proterozoic Huronian Supergroup of the north shore of Lake Huron (Fig. 1) is a thick succession of sedimentary and volcanic rocks deposited between about 2,500 and 2,100 Myr ago as discussed by the authors.
Abstract: The early Proterozoic Huronian Supergroup of the north shore of Lake Huron (Fig. 1) is a thick (up to 12,000 m) succession of sedimentary and volcanic rocks deposited between about 2,500 and 2,100 Myr ago1. Here we present a palaeoclimatic interpretation of the Huronian based on approximately 200 major elements analyses of lutites. Most of these are new analyses from the Gowganda and Serpent Formations (Fig. 2). The remainder are from published sources cited in Fig. 4. The composition of lutites from the Huronian Supergroup records an early period of intense, probably tropical, weathering followed by climatic deterioration that culminated in widespread deposition of glaciogenic sediments of the Gowganda Formation. Climatic amelioration followed during deposition of the succeeding Huronian formations. The Huronian succession can be interpreted using a uniformitarian approach in that present day seafloor spreading rates and latitude-related climatic variations are compatible with available geochronological and palaeomagnetic data.

4,822 citations

Journal ArticleDOI
TL;DR: It is found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit, and that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference.
Abstract: Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation, reweighting, and combination techniques, affect classification error. We provide a bias and variance decomposition of the error to show how different methods and variants influence these two terms. This allowed us to determine that Bagging reduced variance of unstable methods, while boosting methods (AdaBoost and Arc-x4) reduced both the bias and variance of unstable methods but increased the variance for Naive-Bayes, which was very stable. We observed that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference. Voting variants, some of which are introduced in this paper, include: pruning versus no pruning, use of probabilistic estimates, weight perturbations (Wagging), and backfitting of data. We found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit. We measure tree sizes and show an interesting positive correlation between the increase in the average tree size in AdaBoost trials and its success in reducing the error. We compare the mean-squared error of voting methods to non-voting methods and show that the voting methods lead to large and significant reductions in the mean-squared errors. Practical problems that arise in implementing boosting algorithms are explored, including numerical instabilities and underflows. We use scatterplots that graphically show how AdaBoost reweights instances, emphasizing not only “hard” areas but also outliers and noise.

2,686 citations

Journal ArticleDOI
01 Oct 1995-Geology
TL;DR: In this article, the authors used ternary diagrams to determine the amount of K addition, premetasomatized sediment composition, and composition of provenance areas, which can be compared with the mineralogy of recent soil profiles and thus, climate and topographic conditions determined for past weathering events.
Abstract: Lutites are commonly metasomatized during diagenesis, but the analysis presented here accounts for most postdepositional change. Potassium metasomatism is particularly common, and typically involves the conversion of kaolin (residual weathering product) to illite by reaction with K + -bearing pore waters. Sandstones also undergo K metasomatism, which involves the replacement of plagioclase by potassium feldspar. These changes can be identified petrographically and are quantitatively accounted for by techniques discussed herein. Bulk chemical analyses and ternary diagrams are used to determine the amount of K addition, premetasomatized sediment composition, and composition of provenance areas. The premetasomatized mineralogy of paleosols can be compared with the mineralogy of recent soil profiles and thus, climate and topographic conditions determined for past weathering events. Some weathering indices lead to erroneous conclusions because, by excluding K 2 O from consideration, correction cannot be made for metasomatic effects.

2,147 citations