Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 80, P. 103812 - 103812

Published: March 1, 2022

Language: Английский

---

Assessment of long and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake basin

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 75, P. 102968 - 102968

Published: April 17, 2022

Language: Английский

---

Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127747 - 127747

Published: March 24, 2022

Language: Английский

---

Evaluating the association between morphological characteristics of urban land and pluvial floods using machine learning methods

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 99, P. 104891 - 104891

Published: Aug. 22, 2023

Language: Английский

---

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Heliyon, Journal Year: 2023, Volume and Issue: 9(5), P. e16186 - e16186

Published: May 1, 2023

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These models were tested high landslides-prone humid sub-tropical type Upper Tista basin Darjeeling-Sikkim Himalaya by integrating GIS remote sensing. The inventory map consisting 477 locations was prepared, about 70% all data utilized training model, 30% used to validate it after training. A total fourteen triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance stream, road, NDVI, LULC, rainfall, modified fournier lithology) taken into consideration preparing LSMs. multicollinearity statistics revealed no collinearity problem among causative factors this study. Based on FR, MIV, IOE, SI, EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, 14.17% areas, respectively, identified very landslide-prone zones. also that IOE model has highest accuracy 95.80%, followed SI (92.60%), MIV (92.20%), FR (91.50%), (89.90%) models. Consistent with actual distribution landslides, high, medium hazardous zones stretch along River major roads. suggested have enough usage mitigation long-term land use planning study area. Decision-makers local planners may utilise study's findings. techniques determining can be employed other Himalayan regions manage evaluate hazards.

Language: Английский

---

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

Natural Hazards Research, Journal Year: 2023, Volume and Issue: 3(3), P. 420 - 436

Published: May 19, 2023

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm socioeconomic environment. main purpose this study is to investigate comparative utility six machine learning algorithms improve flood susceptibility ensemble techniques' capability elucidate underlying patterns floods make a more accurate prediction susceptibilities basin. In present scenario, frequency area becomes high with heavy sudden rainfall, so it essential mitigation measure. At First, spatial database was built 200 locations sixteen influencing factors, its process help Geographic Information System (GIS) environment build up different models applying techniques. It has found zone using learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), Bagging helping GIS model validation Receiver Operating Characteristic Curve (ROC). Afterward, all gate accuracy zone. calculated under very 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% 13.41% for ANN, SVM, RF, REPTree, LR Bagging, respectively. Finally, ROC curve, Standard (SE), Confidence Interval (CI) at 95 per cent were used assess compare performance models. obtained results indicate that are highly accepted Area Under (AUC) between 0.889 (LR) 0.926 (Ensemble). After application, ROC, Ensemble suited highest compared other projecting area. curve AUC values 0.918 0.926, SE (0.023, 034), narrowest CI (95 cent) (0.873–0.962, 0.859–0.993) whereas (the ROC) value (0.914, 0.919), both training datasets. ensembling, result shows susceptible located lower part area, lie 4.46 6.00 result. areas comprise low height belong Murarai I, II, Suti I II C.D. block West Bengal. current will policymakers researcher determine conditioning problems prospects.

Language: Английский

---

Unraveling the Interactions between Flooding Dynamics and Agricultural Productivity in a Changing Climate

Sustainability, Journal Year: 2024, Volume and Issue: 16(14), P. 6141 - 6141

Published: July 18, 2024

Extreme precipitation and flooding frequency associated with global climate change are expected to increase worldwide, major consequences in floodplains areas susceptible flooding. The purpose of this review was examine the effects events on changes soil properties their agricultural production. Flooding is caused by natural anthropogenic factors, can be amplified interactions between rainfall catchments. impacts structure aggregation altering resistance slaking, which occurs when aggregates not strong enough withstand internal stresses rapid water uptake. disruption enhance erosion sediment transport during contribute sedimentation bodies degradation aquatic ecosystems. Total precipitation, flood discharge, total main factors controlling suspended mineral-associated organic matter, dissolved particulate matter loads. Studies conducted paddy rice cultivation show that flooded reduced conditions neutralize pH but reversible upon draining soil. In soil, nitrogen cycling linked decreases oxygen, accumulation ammonium, volatilization ammonia. Ammonium primary form inorganic porewaters. floodplains, nitrate removal enhanced high denitrification intermittent provides necessary anaerobic conditions. soils, reductive dissolution minerals release phosphorus (P) into solution. Phosphorus mobilized events, leading increased availability first weeks waterlogging, generally time. Rainstorms promote subsurface P-enriched particles, colloidal P account for up 64% tile drainage water. Anaerobic microorganisms prevailing utilize alternate electron acceptors, such as nitrate, sulfate, carbon dioxide, energy production decomposition. metabolism leads fermentation by-products, acids, methane, hydrogen sulfide, influencing pH, redox potential, nutrient availability. Soil enzyme activity presence various microbial groups, including Gram+ Gram− bacteria mycorrhizal fungi, affected Waterlogging β-glucosidase acid phosphomonoesterase increases N-acetyl-β-glucosaminidase Since these enzymes control hydrolysis cellulose, phosphomonoesters, chitin, moisture content impact direction magnitude supply oxygen submerged plants limited because its diffusion extremely low, mitochondrial respiration plant tissues. Fermentation only viable pathway plants, which, under prolonged waterlogging conditions, inefficient results death. Seed germination also impaired stress due decreased sugar phytohormone biosynthesis. sensitivity different crops varies significantly across growth stages. Mitigation adaptation strategies, essential management agriculture, resilience through improved practices, amendments rehabilitation techniques, best zero tillage cover crops, development flood-tolerant crop varieties. Technological advances play a crucial role assessing dynamics landscapes. This embarks comprehensive journey existing research unravel intricate interplay production, environment. We synthesize available knowledge address critical gaps understanding, identify methodological challenges, propose future directions.

Language: Английский

---

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(2)

Published: Jan. 4, 2024

Language: Английский

---

Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability

Cogent Food & Agriculture, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 7, 2025

Accurate insights into the spatial distribution of cultivated areas, land use for effective agricultural management, and improvement food security planning, especially in developing countries. Therefore, this study examined impact changes population growth on wheat crop productivity. First, by incorporating more than three decades satellite data (1990–2022) different Landsat missions with machine learning algorithms, high-confidence classes were defined features, including cropland. Second, grown area was identified using cropland extraction based acreage assessment method (CLE-WAAM). Third, dynamics applying an exponential model to forecast predict demand. These findings necessitate integrated methodological development demand supply mechanisms two-step floating catchment (2SFCA) approach a thorough analysis socioeconomic developments. The results revealed that transformed non-cropland, percentage 8.01. A 79% rise occured between 1990 2022, projected increase 112% 2030. Specifically, cultivation decreased 28%, despite stagnant parameters observed since 2000. proposed contributes efficiently United Nations' sustainable goal (02: Zero Hunger) satellite, geospatial, statistical integration.

Language: Английский

---

Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods

Soft Computing, Journal Year: 2021, Volume and Issue: 25(14), P. 9325 - 9346

Published: May 26, 2021

Language: Английский

---