Application of novel data-mining technique based nitrate concentration susceptibility prediction approach for coastal aquifers in India

Journal of Cleaner Production, Год журнала: 2022, Номер 346, С. 131205 - 131205

Опубликована: Март 4, 2022

Язык: Английский

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Landslide susceptibility modeling by interpretable neural network

Communications Earth & Environment, Год журнала: 2023, Номер 4(1)

Опубликована: Май 10, 2023

Abstract Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute slope stability. Artificial neural networks (ANN) have been shown improve prediction accuracy but largely uninterpretable. Here we introduce an additive ANN optimization framework assess landslide susceptibility, as well dataset division outcome interpretation techniques. We refer our approach, which features full interpretability, high accuracy, generalizability low model complexity, superposable network (SNN) optimization. validate approach by training models on inventories from three different easternmost Himalaya regions. Our SNN outperformed physically-based statistical achieved similar performance state-of-the-art deep networks. The found the product of precipitation hillslope aspect be important primary contributors highlights importance strong slope-climate couplings, along with microclimates, occurrences.

Язык: Английский

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Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

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Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios

Journal of Cleaner Production, Год журнала: 2021, Номер 320, С. 128713 - 128713

Опубликована: Авг. 20, 2021

Язык: Английский

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Threats of climate change and land use patterns enhance the susceptibility of future floods in India

Journal of Environmental Management, Год журнала: 2021, Номер 305, С. 114317 - 114317

Опубликована: Дек. 24, 2021

Язык: Английский

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Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India

Geocarto International, Год журнала: 2021, Номер 37(23), С. 6713 - 6735

Опубликована: Июль 12, 2021

Flood-susceptibility mapping is an important component of flood risk management to control the effects natural hazards and prevention injury. We used a remote-sensing geographic information system (GIS) platform machine-learning model develop susceptibility map Kangsabati River Basin, India where flash common due monsoon precipitation with short duration high intensity. And in this subtropical region, climate change's impact helps influence distribution rainfall temperature variation. tested three models-particle swarm optimization (PSO), artificial neural network (ANN), deep-leaning (DLNN)-and prepared final classify flood-prone regions study area. Environmental, topographical, hydrological, geological conditions were included models, was selected based on relations between potentiality causative factors multi-collinearity analysis. The results validated evaluated using area under receiver operating characteristic (ROC) curve (AUC), which indicator current state environment value >0.95 implies greater floods. AUC values for ANN, DLNN, PSO training datasets 0.914, 0.920, 0.942, respectively. Among these showed best performance 0.942. approach applicable eastern part India, allow mitigation help improve region.

Язык: Английский

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Flood susceptibility mapping using meta-heuristic algorithms

Geomatics Natural Hazards and Risk, Год журнала: 2022, Номер 13(1), С. 949 - 974

Опубликована: Апрель 11, 2022

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important management measures. We compute the map Kaiser watershed in Iran using machine learning models such as support vector (SVM), Particle swarm optimization (PSO), genetic algorithm (GA) along with ensembles (PSO-GA SVM-GA). The application of assessment mapping analyzed, future research suggestions presented. model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, cover, normalize differences vegetation index (NDVI), convergence (CI), topographical wetness (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness (TRI), surface texture (TST), geology stream power (SPI) inventory data which later divided by 70% training 30% validated model. output evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, receiver operating curve (ROC). evaluation method shows robust results from (0.839), particle (0.851), (0.874), SVM-GA (0.886), PSO-GA (0.902). Compared have done some methods commonly used this assessment. A high-quality, informative database essential classification types that very helpful improve performances. performance ensemble better than model, yielding high degree accuracy (AUC-0.902%). Our approach, therefore, provides novel studies other watersheds.

Язык: Английский

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A coupled novel framework for assessing vulnerability of water resources using hydrochemical analysis and data-driven models

Journal of Cleaner Production, Год журнала: 2022, Номер 336, С. 130407 - 130407

Опубликована: Янв. 6, 2022

Язык: Английский

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Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu

Remote Sensing, Год журнала: 2023, Номер 15(3), С. 798 - 798

Опубликована: Янв. 31, 2023

Landslide is a natural disaster that seriously affects human life and social development. In this study, the characteristics effectiveness of convolutional neural network (CNN) conventional machine learning (ML) methods in landslide susceptibility assessment (LSA) are compared. Six ML used study Adaboost, multilayer perceptron (MLP-NN), random forest (RF), naive Bayes, decision tree (DT), gradient boosting (GBDT). First, basic knowledge structures CNN methods, steps LSA introduced. Then, 11 conditioning factors three categories Hongxi River Basin, Pingwu County, Mianyang City, Sichuan Province chosen to build train, validation, test samples. The models constructed based on these For comparison, indicator statistical maps (LSMs) used. result shows can obtain highest accuracy (86.41%) AUC (0.9249) LSA. represented by mean variance TP TN perform more firmly possibility occurrence. Furthermore, LSMs show all successfully identify most points, but for areas with low frequency landslides, some insufficient. model demonstrates better results recognition landslides’ cluster region, also related convolution operation takes surrounding environment information into account. higher concentrative great significance prevention mitigation, which help efficient use material resources. Although performs than other there still limitations, identification low-cluster landside be enhanced improving model.

Язык: Английский

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Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Land, Год журнала: 2023, Номер 12(2), С. 468 - 468

Опубликована: Фев. 13, 2023

Landslides are prevalent in the Western Ghats, and incidences that happened 2021 Koottickal area of Kottayam district (Western Ghats) resulted loss 10 lives. The objectives this study to assess landslide susceptibility high-range local self-governments (LSGs) using analytical hierarchy process (AHP) fuzzy-AHP (F-AHP) models compare performance existing susceptible maps. This never witnessed any massive landslides dimension, which warrants necessity relooking into landslide-susceptible models. For AHP F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, satellite image-derived indices such as normalized difference index (NDRLI), water (NDWI), burn ratio (NBR), soil-adjusted vegetation (SAVI). zones categorized three: low, moderate, high. validation maps created receiver operating characteristic (ROC) technique ascertained performances AHP, F-AHP, TISSA excellent, with an under ROC curve (AUC) value above 0.80, NCESS map acceptable, AUC 0.70. Though is negligible, prepared model has better (AUC = 0.889) than 0.872), 0.867), 0.789) employing other matrices accuracy, mean absolute error (MAE), root square (RMSE) also confirmed (0.869, 0.226, 0.122, respectively) performance, followed by (0.856, 0.243, 0.147, respectively), (0.855, 0.249, 0.159, (0.770, 0.309, 0.177, most landslide-inducing identified through LULC, NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, Koruthodu LSGs highly landslides. identification areas diversified techniques will aid decision-makers identifying critical infrastructure at risk alternate routes for emergency evacuation people safer terrain during exigency.

Язык: Английский

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