Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 873 - 873

Published: Feb. 4, 2023

Climate change may cause severe hydrological droughts, leading to water shortages which will require be assessed using high-resolution data. Gravity Recovery and Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution monitor drought, but its coarse resolution (1°) limits applications small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) Artificial Neural Network (ANN) downscale GRACE TWSA from 1° 0.25°. The findings revealed that XGBoost model outperformed ANN with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) Root Mean Square Error (RMSE) (5.22 mm), Absolute (MAE) (2.75 mm) between predicted GRACE-derived TWSA. Further, Deficit Index (WSDI) WSD (Water Deficit) were used determine severity episodes respectively. results WSDI exhibited strong agreement when compared Standardized Precipitation Evapotranspiration (SPEI) at different time scales (1-, 3-, 6-months) self-calibrated Palmer Drought Severity (sc-PDSI). Moreover, IBIS had experienced increasing drought episodes, e.g., eight detected within years 2010 2016 −1.20 −1.28 total −496.99 mm −734.01 mm, Partial Least Regression (PLSR) climatic variables indicated potential evaporation largest influence on after precipitation. this study helpful for drought-related decision-making in IBIS.

Language: Английский

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Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

Heliyon, Journal Year: 2023, Volume and Issue: 9(2), P. e13212 - e13212

Published: Jan. 26, 2023

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 2017. Landsat data (Thematic mapper [TM]), 2000 2010 (Enhanced Thematic Mapper [ETM+]), 2013 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into classes termed snow, water, barren land, built-up area, forest, vegetation. method was built multitemporal images machine learning Support Vector Machine (SVM), Naive Bayes Tree (NBT) Kernel Logistic Regression (KLR). According results, area decreased 19,360 km2 (26.0%) 18,784 (25.2%) 2010, while increased 18,640 (25.0%) 26,765 (35.9%) due "One billion tree Project". our findings, SVM performed better than KLR NBT on all three accuracy metrics (recall, precision, accuracy) F1 score >0.89. demonstrated that concurrent reforestation land areas improved methods of sustaining RS GIS everyday forestry organization practices Khyber Pakhtun Khwa (KPK), Pakistan. results beneficial, especially at decision-making level for local or provincial government KPK understanding global scenario regional planning.

Language: Английский

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Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: May 3, 2023

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and cover (LULC) changes in Cholistan Thal deserts Punjab, Pakistan. plotted distribution of LULC throughout desert terrain for years 1990, 2006 2022. The Random Forest methodology was utilized classify data obtained from Landsat 5 (TM), 7 (ETM+) 8 (OLI/TIRS), as well ancillary data. maps generated using this method have an overall accuracy more than 87%. CA-Markov forecast usage 2022, were projected 2038 by extending patterns seen A CA-Markov-Chain developed simulating long-term landscape at 16-year time steps 2022 2038. Analysis urban sprawl carried out (RF). Through Chain analysis, we can expect that high density low-density residential areas will grow 8.12 12.26 km2 18.10 28.45 2038, inferred occurred 1990 showed there would be increased urbanization terrain, with probable development croplands westward northward, growth centers. findings potentially assist management operations geared towards conservation wildlife eco-system region. This also a reference other studies try project arid are undergoing land-use comparable those study.

Language: Английский

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Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms

Frontiers in Environmental Science, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 5, 2023

The landscape of Pakistan is vulnerable to flood and periodically affected by floods different magnitudes. aim this study was aimed assess the flash susceptibility district Jhelum, Punjab, using geospatial model Frequency Ratio Analytical Hierarchy Process. Also, considered eight most influential flood-causing parameters are Digital Elevation Model, slop, distance from river, drainage density, Land use/Land cover, geology, soil resistivity (soil consisting rocks formation) rainfall deviation. data collected weather stations in vicinity area. Estimated weight allotted each flood-inducing factors with help AHP FR. Through use overlay analysis, were brought together, value density awarded maximum possible score. According several areas region based on have been classified zones viz, very high risk, moderate low risk. In light results obtained, 4% area that accounts for 86.25 km 2 at risk flood. like Bagham, Sohawa, Domeli, Turkai, Jogi Tillas, Chang Wala, Dandot Khewra located elevation. Whereas Potha, Samothi, Chaklana, Bagrian, Tilla Jogian, Nandna, Rawal high-risk damaged badly history This first its kind conducted Jhelum District provides guidelines disaster management authorities response agencies, infrastructure planners, watershed management, climatologists.

Language: Английский

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Identification of time-varying wetlands neglected in Pakistan through remote sensing techniques

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(29), P. 74031 - 74044

Published: May 18, 2023

Language: Английский

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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(5), P. 928 - 928

Published: March 6, 2024

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, ecologically significant wetland ecosystem in Pakistan, using advanced geospatial machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection risk mapping examine moisture variability, modifications, area changes proximity-based threats over two decades. The random forest algorithm attained highest accuracy (89.5%) for classification based on rigorous k-fold cross-validation, with a training 91.2% testing 87.3%. demonstrates model’s effectiveness robustness vulnerability modeling area, showing 11% shrinkage open bodies since 2000. Inventory zoning revealed 30% present-day areas under moderate high vulnerability. cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic like 29 million population growth surrounding Lake. research integrating satellite analytics, algorithms spatial generate actionable insights into guide conservation planning. findings robust baseline inform policies aimed at ensuring health sustainable management Lake wetlands human climatic that threaten functioning these ecosystems.

Language: Английский

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Flood susceptibility mapping contributes to disaster risk reduction: A case study in Sindh, Pakistan

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 108, P. 104503 - 104503

Published: April 23, 2024

Floods are a widespread and damaging natural phenomenon that causes harm to human lives, resources, property has agricultural, eco-environmental, economic impacts. Therefore, it is crucial perform flood susceptibility mapping (FSM) identify susceptible zones mitigate reduce damage. This study assessed the damage caused by 2022 flash in Sindh identified flood-susceptible based on frequency ratio (FR) analytical hierarchy process (AHP) models. Flood inventory maps were generated, containing 150 sampling points, which manually selected from Landsat imagery. The points split into 70% for training 30% validating results. Furthermore, fourteen conditioning factors considered analysis developing FSM. final FSM categorized five zones, representing levels very low high. results areas under high Ghotki (FR 4.42% AHP 5.66%), Dadu 21.40% 21.29%), Sanghar 6.81% 6.78%). Ultimately, accuracy was evaluated using receiver operating characteristics area curve method, resulting 82%, 83%), 91%, 90%), 96%, 95%). enhances scientific understanding of impacts across diverse regions emphasizes importance accurate informed decision-making. findings provide valuable insights supportive policymakers, agricultural planners, stakeholders engaged risk management adverse consequences floods.

Language: Английский

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Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

The present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect, curvature, drainage network, LULC, precipitation, geology, Lineament, NDVI, road, soil texture, that have been prepared integrated into ArcGIS 10.8. reliability applied models' results was validated using Area Under Receiver Operating Characteristics (AUROC). GWPZ were reclassified five classes, i.e. very low, medium, high, high zone. area occupied by mentioned classes WOE are low (10.14%), (19.58%), medium (26.75%), (27.10%), (16.40%), while FR (20.93%), (32.38%), (18.92%), (13.13%), (14.61%) IV (14.41%), (17.17%), (29.01%), (25.85%), High (13.53%). Success Rate Curve WOE, FR, 0.86, 0.91, 0.87, Predicted values 0.89, 0.93, 0.90, respectively. revealed all statistical performed well delineate GWPZ. However, use technique strongly encouraged evaluate GWPZ, its findings especially useful for managing resources urban planning. Our approaches assessing mapping can be any similar scenarios recommended as a helpful tool policymakers manage groundwater.

Language: Английский

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Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques

Cogent Food & Agriculture, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 16, 2024

Agricultural Land Suitability Analysis plays a pivotal role in sustainable land use planning, aiding decision-makers identifying areas most conducive to agriculture. This study employs systematic approach integrating Analytical Hierarchy Process and Multi-Criteria Decision techniques assess prioritize the suitability of agricultural Southern Punjab (Multan region). The methodology involves defining clear objectives, relevant criteria sub-criteria, establishing hierarchical structure conducting pairwise comparisons determine relative importance each factor. Our outcomes indicated that almost 43% area was highly suitable for agriculture, 27% moderately suitable, 16% marginally 8% less 6% not agriculture area. All lands had silty clay or type soil, which sandy loam soil Multan region. output is comprehensive map identifies Sensitivity analysis validation are incorporated enhance robustness reliability results. provides valuable tool planners policymakers make informed decisions regarding allocation, contributing practices resource management.

Language: Английский

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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 24, 2025

The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for planners decision-makers. current research was carried out to study use cover (LULC) changes associated LST patterns planned city (Kabul) unplanned (Jalalabad), Afghanistan, using Support Vector Machine (SVM) Landsat data from 1998 2018. Future LULC were predicted 2028 2038 Cellular Automata-Markov (CA-Markov) Artificial Neural Network (ANN) models. results clearly emphasize different between Kabul Jalalabad. Between 2018, built-up areas Jalalabad increased by 16% 30%, respectively, while bare soil vegetation decreased 15% 1% 4% 30% showed highest seasonal annual LST, followed vegetation. maximum occurred during summer both cities predictions that (48% 55% 2018) will increase approximately 59% 68% 79% Jalalabad, respectively. Similarly, simulations percentage with higher (> 35°C) would (0% 5% 22% 43% 2038, Kabul's shows lower than Jalalabad's city, primarily due urbanization greater center. Urban should limit development reduce potential impacts high temperatures.

Language: Английский

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