GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE DOI Creative Commons
Sarva T. Pulla,

Hakan Yasarer,

Lance D. Yarbrough

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2247 - 2247

Published: April 24, 2023

Monitoring and managing groundwater resources is critical for sustaining livelihoods supporting various human activities, including irrigation drinking water supply. The most common method of monitoring well level measurements. These records can be difficult to collect maintain, especially in countries with limited infrastructure resources. However, long-term data collection required characterize evaluate trends. To address these challenges, we propose a framework that uses from the Gravity Recovery Climate Experiment (GRACE) mission downscaling models generate higher-resolution (1 km) predictions. designed flexible, allowing users implement any machine learning model interest. We selected four models: deep model, gradient tree boosting, multi-layer perceptron, k-nearest neighbors regressor. effectiveness framework, offer case study Sunflower County, Mississippi, using validate Overall, this paper provides valuable contribution field resource management by demonstrating remote sensing techniques improve resource, those who seek faster way begin use datasets applications.

Language: Английский

Geospatial insights into groundwater contamination from urban and industrial effluents in Faisalabad DOI Creative Commons
Abdul Quddoos,

Khalid Muhmood,

Iram Naz

et al.

Discover Water, Journal Year: 2024, Volume and Issue: 4(1)

Published: July 24, 2024

Abstract Groundwater remains the most dependable resource for various essential uses such as drinking, cleansing, agricultural irrigation, and industrial applications. In urban areas, dependency on groundwater to meet water demands is significant. However, this faces threats from overuse poor management, leading a degradation in quality primarily due unchecked release of household wastes. The escalation activities rapid growth have amplified volume wastewater, adversely affecting purity freshwater sources within aquifers. This investigation focuses evaluating impact effluents city Faisalabad. main contributors pollution include indiscriminate disposal through unlined drains extensive application chemical agents agriculture, fertilizers, pesticides. To understand physiochemical properties both, drain groundwater, samples were collected at distances 50 m, 100 150 m outlets. study utilized Geographic Information Systems (GIS) accurately map analyze distribution contaminants. Parameters pH, electrical conductivity (EC), total dissolved solids (TDS), hardness, bicarbonates, calcium magnesium chloride levels examined. findings indicated that contaminant highest increased concentration closer they drainage sources, with exception pH levels. All exceeded World Health Organization's (WHO) safe limits, deeming them unfit use. finding indicates widespread contamination, posing significant public health risks highlighting urgent need improved waste management treatment practices It underscores critical importance implementing effective control measures safeguard ensure security region. notable correlation was observed between pollutants key indicators EC, TDS, their role deteriorating aquifer quality. Moreover, exhibited pollutant concentrations compared those taken further away, distances.

Language: Английский

Citations

12
Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning DOI
Shoaib Ali, Jiangjun Ran, Behnam Khorrami

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101100 - 101100

Published: Jan. 23, 2024

Language: Английский

Citations

11
Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review DOI
Vahid Nourani, Nardin Jabbarian Paknezhad, A. W. M Ng

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101113 - 101113

Published: Feb. 10, 2024

Language: Английский

Citations

11
Integrated study of GIS and Remote Sensing to identify potential sites for rainwater harvesting structures DOI

Xingsheng Du,

Aqil Tariq, Fakhrul Islam

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 134, P. 103574 - 103574

Published: Feb. 20, 2024

Language: Английский

Citations

10
Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities DOI
Vijendra Kumar, Kul Vaibhav Sharma, Quoc Bao Pham

et al.

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(6), P. 4251 - 4288

Published: March 5, 2024

Language: Английский

Citations

10
The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China DOI Creative Commons
Wanqiu Li, Lifeng Bao, Guobiao Yao

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 9, 2024

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as example, data from Gravity Recovery Climate Experiment (GRACE) GRACE Follow-On (GRACE-FO) used to invert GWS January 2003 December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ volume level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), impact factors, such precipitation human activities, which also analyzed. To predict short-time changes of GWS, Support Vector Machines (SVM) adopted three commonly methods Long Short-Term Memory (LSTM), Singular Spectrum (SSA), Auto-Regressive Moving Average (ARMA), comparison. results show that: (1) loss intensity western significantly greater than those in coastal areas. From 2006, increased sharply; during 2007 2014, there exists a rate - 5.80 ± 2.28 mm/a GWS; linear trend change 5.39 3.65 2015 2022, may be mainly due effect South-to-North Water Diversion Project. correlation coefficient between WGHM 0.67, consistent level. (2) has higher positive monthly Precipitation Climatology Project (GPCP) considering time delay after moving average, similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, influencing facotrs annual analyzed, including consumption mining, farmland irrigation 0.80, 0.71, respectively. (3) For prediction, SVM method analyze, training samples 180, 204 228 months established goodness-of-fit all 0.97. coefficients 0.56, 0.75, 0.68; RMSE 5.26, 4.42, 5.65 mm; NSE 0.28, 0.43, 0.36, performance model better other short-term prediction.

Language: Английский

Citations

9
Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha DOI
Abhijeet Das

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

Citations

1
Exploring hazard quotient, cancer risk, and health risks of toxic metals of the Mehmood Booti and Lakhodair landfill groundwaters, Pakistan DOI

Rose Mary,

Rabiya Nasir,

Asifa Alam

et al.

Environmental Nanotechnology Monitoring & Management, Journal Year: 2023, Volume and Issue: 20, P. 100838 - 100838

Published: June 2, 2023

Language: Английский

Citations

22
A XGBoost-Based Downscaling-Calibration Scheme for Extreme Precipitation Events DOI
Honglin Zhu, Huizeng Liu, Qiming Zhou

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 12

Published: Jan. 1, 2023

Extreme precipitation events have caused severe societal, economic and environmental impacts through the disasters of floods, flash-floods landslides. However, coarse-resolution satellite-derived data makes it difficult to quantitatively capture certain fine-scale heavy rainfall process. Therefore, improve spatial resolution accuracy satellite-based extremes, a downscaling-calibration scheme based on eXtreme Gradient Boosting (XGBoost_DC) was proposed in this study, where XGBoost algorithm applied both downscaling calibration procedures. The performance XGBoost_DC evaluated with other two comparative methods, which only used either (XGBoost_Spline) or (Spline_XGBoost) results showed that: (i) achieved best performance, as obtained highest well reproduced occurrence distribution during typhoon events. (ii) could variations precipitation. Although Spline_XGBoost slightly worse than XGBoost_DC, significantly underestimated variability. (iii) model assessment between illustrated essential contribution process, improved our understanding capability machine learning reproducing variance These findings imply that can be for generating high-resolution high-quality extremes events, would benefit water flood management, various applications hydrological meteorological modelling.

Language: Английский

Citations

22
County-level corn yield prediction using supervised machine learning DOI Creative Commons
Shahid Nawaz Khan,

Abid Nawaz Khan,

Aqil Tariq

et al.

European Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 56(1)

Published: Sept. 5, 2023

The main objectives of this study are (1) to compare several machine learning models predict county-level corn yield in the area and (2) feasibility for in-season prediction. We acquired remotely sensed vegetation indices data from moderate resolution imaging spectroradiometer using Google Earth Engine (GEE). Vegetation a span 15 years (2006–2020) were processed downloaded GEE months corresponding crop growth (April–October). compared nine yield. Furthermore, we analyzed prediction performance top three models. results show that partial least square regression (PLSR) outperformed other by achieving highest training testing performance. area's PLSR, support vector (SVR) ridge regression. For prediction, SVR model performed comparatively well R2 = 0.875. can both (best 0.875) end-of-season 0.861) with satisfactory indicate remote sensing be used before harvest decent This provide useful insights terms food security early decision making related climate change impacts on security.

Language: Английский

Citations

21