Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review DOI Creative Commons
Hamid Mohebzadeh, Asim Biswas,

Ramesh Rudra

и другие.

Geosciences, Год журнала: 2022, Номер 12(12), С. 429 - 429

Опубликована: Ноя. 22, 2022

Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully is required plan control strategies relevant soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due their vast capabilities. In this context, paper sought review modeling procedure using ML models, including datasets and model development validation. The results showed that elevation, slope, curvature, rainfall land use/cover were most important factors GESM. It also concluded although predict locations zones gullying reasonably well, performance ranking such methods difficult because they yield different based on quality training dataset, structure indicators. Among techniques, random forest (RF) support vector (SVM) are widely used GESM, which show promising results. Overall, improve prediction use data-mining techniques dataset an ensemble estimation approach recommended. Furthermore, evaluation other types erosion, as rill–interill ephemeral should be subject more studies in future. employment a combination topographic indices recommended accurate extraction trajectories main input some process-based models.

Язык: Английский

Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning DOI Creative Commons
Mariana Dos Santos Toledo Busarello, Anneli Ågren, Florian Westphal

и другие.

Computers & Geosciences, Год журнала: 2025, Номер unknown, С. 105875 - 105875

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives DOI
Asish Saha, Subodh Chandra Pal, Indrajit Chowdhuri

и другие.

Gondwana Research, Год журнала: 2022, Номер 123, С. 68 - 88

Опубликована: Ноя. 14, 2022

Язык: Английский

Процитировано

54
Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake DOI

Dipankar Ruidas,

Subodh Chandra Pal, Asish Saha

и другие.

Marine Pollution Bulletin, Год журнала: 2022, Номер 184, С. 114107 - 114107

Опубликована: Сен. 11, 2022

Язык: Английский

Процитировано

48
Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model DOI Creative Commons
Haoyuan Hong

Ecological Indicators, Год журнала: 2023, Номер 147, С. 109968 - 109968

Опубликована: Фев. 6, 2023

Landslide susceptibility mapping is a meaningful method to avoid and reduce the loss from landslide hazard. The main goal of current paper propose hybrid model explore effect combining Best-first decision tree (BFT) with Bagging, Cascade generalization, Decorate, MultiboostAB, Random SubSpace measure achievement each combination model. Firstly, inventory map was produced using 364 landslides in Yongxin County China, then non-landslide data were generated based on buffer method. Secondly, 255 non-landslides randomly chosen for training rest 109 validation data. Then, fifteen environment factors chosen. Thirdly, Support vector machines (SVM) applied analysis most useful modeling. result demonstrated that all Several statistical indexes used performance, results revealed five models performed better than single BFT BFT-D BFT-B best effective can be adapted susceptibility. maps by will help land use arrangement groundwork expansion County.

Язык: Английский

Процитировано

39
Random Forest–based gully erosion susceptibility assessment across different agro-ecologies of the Upper Blue Nile basin, Ethiopia DOI Creative Commons
Tadesual Asamin Setargie, Atsushi Tsunekawa, Nigussie Haregeweyn

и другие.

Geomorphology, Год журнала: 2023, Номер 431, С. 108671 - 108671

Опубликована: Март 27, 2023

Several environmental factors are known to influence the spatial distribution and susceptibility of gully erosion, yet relative importance interaction these remain little understood in Ethiopia. In this study, we integrated detailed field investigations with high-resolution remote sensing products assess erosion identify its controlling using Random Forest (RF) model six representative watersheds across contrasting (highland, midland, lowland) agro-ecological environments Upper Blue Nile basin Data for 20 were extracted from datasets at eight different pixel resolutions ranging 0.5 30 m a geographic information system environment. About 70 % dataset each watershed randomly selected training validation purposes, respectively. Multicollinearity correlation analyses performed variables collinearity problems explain their statistical relationships among other variables. RF predicted factors. The showed outstanding performance when finest-resolution used. Elevation, height above nearest drainage, runoff curve number-II, distance streams, drainage density, soil type, land use/land cover found be most important gullies all watersheds, irrespective treatment conditions settings. Thus, susceptible was low-lying grazing cultivated lands sensitive high runoff-generation capacity located within short horizontal vertical distances networks. Therefore, basin- watershed-scale management strategies should give priority areas. identification hydrologic parameter predicting direct excess rainfall, as one novel finding which will useful developing improved process-based models.

Язык: Английский

Процитировано

29
Soil Erosion Assessment by RUSLE, Google Earth Engine, and Geospatial Techniques over Rel River Watershed, Gujarat, India DOI
Keval H. Jodhani, Dhruvesh Patel,

N. Madhavan

и другие.

Water Conservation Science and Engineering, Год журнала: 2023, Номер 8(1)

Опубликована: Сен. 26, 2023

Язык: Английский

Процитировано

27
Advanced soil conservation for African drylands: from erosion models to management theories DOI

Suleiman Usman

Pedosphere, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale DOI Creative Commons
Sliman Hitouri, Antonietta Varasano, Meriame Mohajane

и другие.

ISPRS International Journal of Geo-Information, Год журнала: 2022, Номер 11(7), С. 401 - 401

Опубликована: Июль 14, 2022

Gully erosion is a serious threat to the state of ecosystems all around world. As result, safeguarding soil for our own benefit and from actions must guaranteeing long-term viability variety ecosystem services. developing gully susceptibility maps (GESM) both suggested necessary. In this study, we compared effectiveness three hybrid machine learning (ML) algorithms with bivariate statistical index frequency ratio (FR), named random forest-frequency (RF-FR), support vector machine-frequency (SVM-FR), naïve Bayes-frequency (NB-FR), in mapping GHISS watershed northern part Morocco. The models were implemented based on inventory total number 178 points randomly divided into 2 groups (70% used training 30% validation process), 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture (TWI), stream power (SPI), precipitation, distance road, stream, drainage density, land use, lithology). Using equal interval reclassification method, spatial distribution was categorized five different classes, including very high, moderate, low, low. Our results showed that high classes derived using RF-FR, SVM-FR, NB-FR covered 25.98%, 22.62%, 27.10% area, respectively. area under receiver (AUC) operating characteristic curve, precision, accuracy employed evaluate performance these models. Based (ROC), RF-FR achieved best (AUC = 0.91), followed by SVM-FR 0.87), then 0.82), contribution, line Sustainable Development Goals (SDGs), plays crucial role understanding identifying issue “where why” occurs, hence it can serve as first pathway reducing particular area.

Язык: Английский

Процитировано

37
Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China DOI Creative Commons
Donghao Huang, Lin Su, Lili Zhou

и другие.

International Soil and Water Conservation Research, Год журнала: 2022, Номер 11(1), С. 97 - 111

Опубликована: Апрель 17, 2022

As a primary sediment source, gully erosion leads to severe land degradation and poses threat food ecological security. Therefore, identification of susceptible areas is critical the prevention control erosion. This study aimed identify prone using four machine learning methods with derived topographic attributes. Eight attributes (elevation, slope aspect, degree, catchment area, plan curvature, profile stream power index, wetness index) were as feature variables controlling occurrence from digital elevation models different pixel sizes (5.0 m, 12.5 20.0 30.0 m). A inventory map small agricultural in Heilongjiang, China, was prepared through combination field surveys satellite imagery. Each attribute dataset randomly divided into two portions 70% 30% for calibrating validating methods, namely random forest (RF), support vector machines (SVM), artificial neural network (ANN), generalized linear (GLM). Accuracy (ACC), area under receiver operating characteristic curve (AUC), root mean square error (RMSE), absolute (MAE) calculated assess performance predicting spatial distribution susceptibility (GES). The results suggested that selected capable GES area. size m optimal all methods. RF method described relationship between greatest accuracy, it returned highest values ACC (0.917) AUC (0.905) at resolution. also least sensitive resolutions, followed by SVM (ACC = 0.781–0.891, 0.724–0.861) ANN 0.744–0.808, 0.649–0.847). GLM performed poorly this 0.693–0.757, 0.608–0.703). Based on determined (RF + m), 16% has very high level classes, whereas high, moderate, low levels make up approximately 24%, 30%, 31% respectively. Our demonstrate assessment can successfully erosion, providing reference information future soil conservation plans management. In addition, (resolution) key consideration when preparing suitable datasets assessment.

Язык: Английский

Процитировано

34
Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation DOI
Asish Saha, Subodh Chandra Pal, Indrajit Chowdhuri

и другие.

Ecological Informatics, Год журнала: 2022, Номер 69, С. 101653 - 101653

Опубликована: Апрель 27, 2022

Язык: Английский

Процитировано

34