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Pirasteh S, Fang Y, Mafi-Gholami D, Abulibdeh A, Nouri-Kamari A, Khonsari N. Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 930:172744. [PMID: 38685429 DOI: 10.1016/j.scitotenv.2024.172744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024]
Abstract
The evaluation of the vulnerability of coupled socio-ecological systems is critical for addressing and preventing the adverse impacts of various environmental hazards and devising strategies for climate change adaptation. The initial step in vulnerability assessment involves exposure assessment, which entails quantifying and mapping the risks posed by multiple environmental hazards, thereby offering valuable insights for the implementation of vulnerability assessment methodologies. Consequently, this study sought to model the exposure of coupled social-ecological systems in mountainous regions to various environmental hazards. By a set of socio-economic, climatic, geospatial, hydrological, and demographic data, as well as satellite imagery, and examining 11 hazards, including droughts, pests, dust storms, winds, extreme temperatures, evapotranspiration, landslides, floods, wildfires, and social vulnerability, this research employed machine learning (ML) techniques and the fuzzy analytical hierarchy process (FAHP). Expert opinions were utilized to guide hazard weighting and calculate the exposure index (EI). Through the precise spatial mapping of EI variations across the socio-ecological systems in mountainous areas, this investigation provides insights into vulnerability to multiple environmental hazards, thereby laying the groundwork for future endeavors in supporting national-level vulnerability assessments aimed at fostering sustainable environments. The findings reveal that social vulnerability and pests receive the highest weighting, while floods and landslides are ranked lower. All hazards demonstrate significant correlations with the EI, with droughts exhibiting the strongest correlation (r > 0.81). Spatial analysis indicates a north-south gradient in forest exposure, with southern regions showing higher exposure hotspots (EI 29.08) compared to northern areas (EI 10.60). Validation based on Area Under Curve (AUC) and Consistency Rate (CR) in FAHP demonstrates robustness, with AUC values exceeding 0.78 and CR values below 0.1. Considering the anticipated intensification of hazards, management strategies should prioritize reducing social vulnerability, restore degraded areas using drought-resistant species, combat pests, and mitigate desertification. By integrating multidisciplinary data and expert opinions, this research contributes to informed decision-making regarding sustainable forest management and climate resilience in mountain ecosystems.
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Affiliation(s)
- Saied Pirasteh
- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, 508 West Huancheng Road, Yuecheng District, Zhejiang Province 312000, China; Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India.
| | - Yiming Fang
- School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China.
| | - Davood Mafi-Gholami
- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, 508 West Huancheng Road, Yuecheng District, Zhejiang Province 312000, China; Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran.
| | - Ammar Abulibdeh
- Applied Geography and GIS Program, Department of Humanities, College of Arts and Sciences, Qatar University, P.O. Box 2713, Doha, Qatar.
| | - Akram Nouri-Kamari
- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, 508 West Huancheng Road, Yuecheng District, Zhejiang Province 312000, China; Department of Environment, Faculty of Natural Resources, University of Tehran, Tehran, Iran.
| | - Nasim Khonsari
- College of Business, Westcliff University, 17877 Von Karman Ave, Irvine, CA 92614, USA.
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Kumar P, Sen Gupta D, Rao K, Biswas A, Ghosh P. Delineation of groundwater potential zones and its extent of contamination from the hard rock aquifers in west-Bengal, India. ENVIRONMENTAL RESEARCH 2024; 249:118332. [PMID: 38331146 DOI: 10.1016/j.envres.2024.118332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 01/20/2024] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
This study evaluates the groundwater potential and quality in the parts of Chhotanagpur Gneissic Complex situated in the East Indian Shield. The region has faced groundwater development challenges for several decades. Therefore, in the study area, it is crucial to address the depletion of both groundwater quality and quantity, as this facilitates the identification of potential uncontaminated groundwater zones. The present study interprets the groundwater potential zones (GWPZ) utilizing an analytical hierarchical process (AHP) integrated with hydrogeochemical analysis. Several thematic maps were prepared to delineate the GPWZ. It has been found that ∼0.6% of the study area has a very good potential zone, 14.4% has good, 52% has moderate, and approximately 32% and 0.9% have low to very low prospective groundwater resources, respectively. The authentication of results was found to be excellent (91.4%) with the Area Under Curve (AUC). Analysis of hydrogeochemical data suggests that Mixed Ca-Na-HCO3, Mixed Ca-Mg-Cl, Ca-HCO3, and Na-Cl are the dominant water types in the study area. The principal component analysis suggests that Na+, Mg2+, Cl-, NO3-, and SO42- significantly contribute to groundwater chemistry. The K-means clustering and hierarchical cluster analysis classified groundwater samples into three clusters based on the hydrogeochemical characteristics. It is inferred that silicate weathering and reverse ion reactions through rock-water interaction control geogenic processes for groundwater chemistry. It is also inferred that regions with poor to unsuitable water quality indexes also have low GWPZ. Further, groundwater for irrigation is also accessed and found unsuitable at some locations. This research contributes to comprehending groundwater characteristics in analogous geological regions globally. Additionally, it assists in implementing preventive actions to mitigate groundwater contamination, consequently lowering health risks and formulating sustainable plans for the future.
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Affiliation(s)
- Prashant Kumar
- Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, 221005, U.P., India
| | - Dev Sen Gupta
- Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, 221005, U.P., India
| | - Khushwant Rao
- Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, 221005, U.P., India
| | - Arkoprovo Biswas
- Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, 221005, U.P., India.
| | - Parthapratim Ghosh
- Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, 221005, U.P., India
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Atasever ÜH, Tercan E. Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:5304-5318. [PMID: 38112873 DOI: 10.1007/s11356-023-31575-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
In order to evaluate the effects of forest fires on the dynamics of the function and structure of ecosystems, it is necessary to determine burned forest areas with high accuracy, effectively, economically, and practically using satellite images. Extraction of burned forest areas utilizing high-resolution satellite images and image classification algorithms and assessing the successfulness of varied classification algorithms has become a prominent research field. This study aims to indicate on the capability of the deep learning-based Stacked Autoencoders method for the burned forest areas mapping from Sentinel-2 satellite images. The Stacked Autoencoders, used in this study as an unsupervised learning method, were compared qualitatively and quantitatively with frequently used supervised learning algorithms (k-Nearest Neighbors (k-NN), Subspaced k-NN, Support Vector Machines, Random Forest, Bagged Decision Tree, Naive Bayes, Linear Discriminant Analysis) on two distinct burnt forest zones. By selecting burned forest zones with contrasting structural characteristics from one another, an objective assessment was achieved. Manually digitized burned areas from Sentinel-2 satellite images were utilized for accuracy assessment. For comparison, different classification performance and quality metrics (Overall Accuracy, Mean Squared Error, Correlation Coefficient, Structural Similarity Index Measure, Peak Signal-to-Noise Ratio, Universal Image Quality Index, and KAPPA metrics) were used. In addition, whether the Stacked Autoencoders method produces consistent results was examined through boxplots. In terms of both quantitative and qualitative analysis, the Stacked Autoencoders method showed the highest accuracy values.
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Affiliation(s)
- Ümit Haluk Atasever
- Department of Geomatics Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey
| | - Emre Tercan
- Department of Traffic Safety, 13th Region, General Directorate of Highways, 07090, Antalya, Turkey.
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Wang Z, Wang J, Li M. Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:1127-1145. [PMID: 38038910 DOI: 10.1007/s11356-023-31262-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/22/2023] [Indexed: 12/02/2023]
Abstract
Predicting groundwater potential is crucial for identifying the spatial distribution of groundwater in a region. It serves as an essential guide for the development, utilization, and protection of groundwater resources. Previous studies have primarily emphasized finding the most accurate prediction model for groundwater potential while giving less attention to the selection of training features and sample sizes. This study aims to predict groundwater potential within Qinghai Province using automated machine learning technology and assess the influence of sample sizes and feature selection on prediction accuracy. Sixteen groundwater conditioning factors were categorized into categorical and numerical variables. Four feature selection modes were utilized as input in training the model. The results indicated that, except for correlations between evaporation and landforms (- 0.8) and precipitation and normalized difference vegetation index (0.8), the Pearson correlation coefficients among the remaining sixteen factors were ≤ 0.5 or ≥ - 0.5. The models XGB-ALL, RF-Entropy, ET-CRITIC, and XGB-PCA yielded accuracy scores of 0.783, 0.685, 0.745, and 0.703, and area under curve (AUC) of 0.819, 0.724, 0.779, and 0.747, respectively. If enough samples are available with the tree model, an increased number of features can improve prediction accuracy. The principal component analysis method showed difficulty in reducing the dimensionality of the input space, while the Entropy method proved efficient. The accuracy and AUC value of the prediction model improved with an increasing number of samples. Training with 8 features and 200 data points achieved an accuracy of 0.745, sufficient to evaluate regional groundwater potential. As for training with 600 samples, the model's performance accuracy rose to 0.9, enabling precise groundwater potential prediction. The outputs of this research can provide decision-makers in groundwater resource management in Qinghai Province with crucial theoretical and practical support. The lessons learned can have future applications in similar situations.
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Affiliation(s)
- Zitao Wang
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianping Wang
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China.
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China.
| | - Mengling Li
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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Moharir KN, Pande CB, Gautam VK, Singh SK, Rane NL. Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India. ENVIRONMENTAL RESEARCH 2023; 228:115832. [PMID: 37054834 DOI: 10.1016/j.envres.2023.115832] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/11/2023] [Accepted: 04/01/2023] [Indexed: 05/16/2023]
Abstract
The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.
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Affiliation(s)
- Kanak N Moharir
- Department of Remote Sensing, Banasthali Vidyapith, Jaipur, India.
| | - Chaitanya B Pande
- Indian Institute of Tropical Meteorology, Pune, India; Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Vinay Kumar Gautam
- Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur, 313001, India
| | - Sudhir Kumar Singh
- K. Banerjee Centre of Atmospheric & Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad Prayagraj-211002, Uttar Pradesh, India
| | - Nitin Liladhar Rane
- Architecture, Vivekanand Education Society's College of Architecture (VESCOA), Mumbai, India
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Wang Z, Wang J, Yu D, Chen K. The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:63991-64005. [PMID: 37059956 DOI: 10.1007/s11356-023-26961-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/08/2023] [Indexed: 04/16/2023]
Abstract
Groundwater is a vital resource in arid areas that sustains local industrial development and environmental preservation. Mapping groundwater potential zones and determining high-potential regions are essential for the responsible use of the local groundwater resource. When utilizing machine learning or deep learning algorithms to forecast groundwater potential in arid areas, difficulties such as inaccurate and overfitting predictions might occur due to a shortage of borehole samples. In this study, a database of groundwater conditioning factors with a size of 275,157 × 9 was created in the Qaidam Basin, and 85 known borehole samples were collected. The groundwater potential was evaluated using a combination of rank sum ratio (RSR), projection pursuit regression (PPR) and random forest (RF) algorithms, resulting in four models: PPR, RSR-PPR, RSR-RF, and RF. Results indicated that the groundwater potential was higher in mountainous regions surrounding the Qaidam Basin and decreased progressively towards the central and northwestern regions where most industries and facilities are located. The two primary factors, according to the PPR and RF models, were evapotranspiration (0.246, 0.225) and landform (0.176, 0.294). In terms of their ability to accurately forecast the borehole samples, the four models ranked as follows: RF > RSR-RF > RSR-PPR > PPR. The accuracy of the four models in the low-potential area was 0.73 (PPR), 0.60 (RSR-PPR), 0.87 (RSR-RF), and 0.80 (RF), respectively. However, the RF model showed overfitting due to a lack of samples, especially in high-potential regions, which limits its applicability. The RSR-RF method was applied directly to evaluate the entire factor database, avoiding the risk of overfitting caused by a limited number of training samples. The results demonstrate that the RSR-RF model is effective for classifying groundwater potential types in samples and mapping groundwater potential of the study area. This research presents a novel approach for groundwater potential predictions in areas with insufficient sample sizes, providing a reference for policymakers and researchers.
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Affiliation(s)
- Zitao Wang
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianping Wang
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China.
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China.
| | - Dongmei Yu
- Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, 810008, China
- Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining, 810008, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Chen
- School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, China
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Khan AN, Kim BW, Rizwan A, Ahmad R, Iqbal N, Kim K, Kim DH. A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management. ACS OMEGA 2023; 8:10806-10821. [PMID: 37008158 PMCID: PMC10061606 DOI: 10.1021/acsomega.2c06854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/01/2023] [Indexed: 06/19/2023]
Abstract
Drilling boreholes for the exploration of groundwater incurs high cost with potential risk of failures. However, borehole drilling should only be done in regions with a high probability of faster and easier access to water-bearing strata, so that groundwater resources can be effectively managed. However, regional strati-graphic uncertainties drive the decision of the optimal drilling location search. Unfortunately, due to the unavailability of a robust solution, most contemporary solutions rely on physical testing methods that are resource intensive. In this regard, a pilot study is conducted to determine the optimal borehole drilling location using a predictive optimization technique that takes strati-graphic uncertainties into account. The study is conducted in a localized region of the Republic of Korea using a real borehole data set. In this study we proposed an enhanced Firefly optimization algorithm based on an inertia weight approach to find an optimal location. The results of the classification and prediction model serve as an input to the optimization model to implement a well-crafted objective function. For predictive modeling a deep learning based chained multioutput prediction model is developed to predict groundwater-level and drilling depth. For classification of soil color and land-layer a weighted voting ensemble classification model based on Support Vector Machines, Gaussian Naïve Bayes, Random Forest, and Gradient Boosted Machine is developed. For weighted voting, an optimal set of weights is determined using a novel hybrid optimization algorithm. Experimental results validate the effectiveness of the proposed strategy. The proposed classification model achieved an accuracy of 93.45% and 95.34% for soil-color and land-layer, respectively. While the mean absolute error achieved by proposed prediction model for groundwater level and drilling depth is 2.89% and 3.11%, respectively. It is found that the proposed predictive optimization framework can adaptively determine the optimal borehole drilling locations for high strati-graphic uncertainty regions. The findings of the proposed study provide an opportunity to the drilling industry and groundwater boards to achieve sustainable resource management and optimal drilling performance.
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Affiliation(s)
- Anam Nawaz Khan
- Department
of Computer Engineering, Jeju National University, , Jeju 63243, Republic of Korea
| | - Bong Wan Kim
- Electronics
and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
| | - Atif Rizwan
- Department
of Computer Engineering, Jeju National University, , Jeju 63243, Republic of Korea
| | - Rashid Ahmad
- Bigdata
Research Center, Jeju National University, Jeju 63243, Republic of Korea
- Department
of Computer Science, COMSATS University
Islamabad, Attock Campus, Attock, 43600, Pakistan
| | - Naeem Iqbal
- Department
of Computer Engineering, Jeju National University, , Jeju 63243, Republic of Korea
| | - Kwangsoo Kim
- Electronics
and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
| | - Do Hyeun Kim
- Advanced
Technology Research Institute, Jeju National
University, Jeju 63243, Republic of Korea
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Kumar M, Singh P, Singh P. Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.101980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Rasool U, Yin X, Xu Z, Rasool MA, Senapathi V, Hussain M, Siddique J, Trabucco JC. Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan. CHEMOSPHERE 2022; 303:135265. [PMID: 35691394 DOI: 10.1016/j.chemosphere.2022.135265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/31/2022] [Accepted: 06/04/2022] [Indexed: 06/15/2023]
Abstract
Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.
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Affiliation(s)
- Umair Rasool
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China; College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China
| | - Xinan Yin
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing, 100875, China
| | - Zongxue Xu
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China.
| | | | - Venkatramanan Senapathi
- Department of Disaster Management, Alagappa University, Kariakudi, 630003, Tamil Nadu, India
| | - Mureed Hussain
- Lasbela University of Agriculture, Water and Marine Sciences, Uthal, Lasbela, Pakistan
| | - Jamil Siddique
- Earth Science Department, Quaid-I-Azam University, Islamabad, Pakistan
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GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria. SUSTAINABILITY 2022. [DOI: 10.3390/su14084668] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.
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