scholarly journals Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1596 ◽  
Author(s):  
S. Vahid Razavi-Termeh ◽  
Abolghasem Sadeghi-Niaraki ◽  
Soo-Mi Choi
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Statistical Models ◽  
Logistic Model ◽  
Characteristic Curve ◽  
Groundwater Potential ◽  
Model Tree ◽  
Potential Map ◽  
Groundwater Potential Map ◽  
Logistic Model Tree

In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

scholarly journals Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 830 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ayub Mohammadi ◽  
Himan Shahabi ◽  
Baharin Bin Ahmad ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Logistic Regression ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Predictive Value ◽  
Logistic Model ◽  
Model Tree ◽  
Cameron Highlands ◽  
Logistic Model Tree

We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.

scholarly journals GIS-Based Groundwater Potential Assessment in Varied Topographic Areas of Mianyang City, Southwestern China, Using AHP

2021 ◽  
Vol 13 (22) ◽  
pp. 4684
Author(s):  
Qing Zhang ◽  
Shuangxi Zhang ◽  
Yu Zhang ◽  
Mengkui Li ◽  
Yu Wei ◽  
...  
Keyword(s):  
Water Supply ◽  
Groundwater Potential ◽  
Southwestern China ◽  
Borehole Data ◽  
Hydrological Data ◽  
Potential Map ◽  
Groundwater Potential Map ◽  
Emergency Water Supply ◽  
Post Disaster ◽  
Geological Background

Mianyang City is located in the varied topographic areas of Sichuan Province in southwestern China and is characterized by a complex geological background. This area is prone to disasters and its varied topography is inconvenient for emergency water storage and supply. Groundwater is essential for alleviating the demand for water and post-disaster emergency water supply in this area. This study applied AHP to integrate remote sensing, geological and hydrological data into GIS for the assessment of groundwater potential, providing a plan for the rational exploitation of groundwater and post-disaster emergency water supply in the area. Nine factors, including the spring calibration related to groundwater, were integrated by AHP after multicollinear checks. As a result, the geology-controlled groundwater potential map was classified into five levels with equal intervals. All the results were validated using borehole data, indicating the following: the areas with yield rates of , 1–20 , and 20–400 accounted for 2.66%, 36.1%, and 39.62%, respectively, whereas the areas with yield rates of 400–4000 and accounted for only 20.88% and 0.75% of the overall area. The flexibility of this quick and efficient method enables its application in other regions with a similar geological background.

scholarly journals Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2742
Author(s):  
Ehsan Kamali Maskooni ◽  
Seyed Amir Naghibi ◽  
Hossein Hashemi ◽  
Ronny Berndtsson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Logistic Model ◽  
Nearest Neighbors ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Boosted Regression Trees ◽  
K Nearest Neighbors ◽  
Model Tree ◽  
Logistic Model Tree

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

scholarly journals Estimation of Dar-Zarrouk parameters and delineation of groundwater potential zones in Karlahi, part of Adamawa Massif, Northeastern Nigeria

Warta Geologi ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 103-112
Author(s):  
S.N. Yusuf ◽  
◽  
J.M. Ishaku ◽  
W.M. Wakili ◽  
◽  
...  
Keyword(s):  
Groundwater Potential ◽  
Groundwater Potential Zones ◽  
North Central ◽  
Longitudinal Conductance ◽  
The North ◽  
Transverse Resistance ◽  
Potential Map ◽  
Groundwater Potential Map ◽  
Potential Groundwater ◽  
Dar Zarrouk Parameters

Karlahi is largely underlain by granites and migmatites gneiss of the Adamawa Massif. The area lies west of Benue Trough and east of Cameroon volcanic line. The aim of this paper is to determine hydraulic properties of water bearing layer using parameters derived from Dar-Zarrouk equation and characterized them into groundwater potential zones. The resistivity values of the weathered and slightly weathered layers which make up the water bearing layers were added and an average was taken and used as the resistivity of the water bearing formation in computation of Dar-Zarrouk parameters in Karlahi area. The values of resistivity of water bearing formation ranged from 18 to 4963 Ωm with an average resistivity value of 549 Ωm and the thickness of the water bearing formation ranges from 21 to 32 m with an average thickness of 24.5 m. Conductivity values range from 0.000201 to 0.05509 (σ) while the longitudinal conductance range from 0.00483 to 1.2363 Ω-1, the transverse resistance ranges from 407 to 123504.3 Ωm2. The hydraulic conductivity and transmissivity values range from 0.14 to 25.87 m/day and 3.28 to 580.4 m2/day respectively. The longitudinal conductance values in Karlahi area revealed poor to good with an average longitudinal conductance value that is moderate. High transverse resistance values are located in the central and southern part of Karlahi area while low values are located in the eastern part. The spatial distribution map of transmissivity in the area revealed moderate to high transmissivity values in the north central part and a negligible to low transmissivity in southern part, extreme northeastern part. The groundwater potential map of Karlahi area shows negligible to weak potential groundwater zones in SW and SE, moderate potential in the central to northern part of Karlahi area.

scholarly journals Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

2019 ◽  
Vol 8 (2S11) ◽  
pp. 202-209
Keyword(s):  
Feature Selection ◽  
Wind Energy ◽  
Condition Monitoring ◽  
Logistic Model ◽  
Turbine Blades ◽  
Fault Classification ◽  
Renewable Energy Resources ◽  
Model Tree ◽  
Tree Classifier ◽  
Logistic Model Tree

Wind energy is one of the essential renewable energy resources because of its consistency due to the development of the technology and relative cost affordability. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large construction, the blades are subjected to various faults and cause the lack of productivity. The downtime can be reduced when they are diagnosed periodically using condition monitoring technique. These are considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and fault classification. In this study, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and the fault classification was carried out using logistic model tree algorithm.

scholarly journals Hydrogeological Study in and around Birendranagar Municipality, Surkhet Valley, Mid-Western Nepal

2020 ◽  
Vol 22 ◽  
pp. 41-48
Author(s):  
Roshani B. C. ◽  
Dinesh Pathak ◽  
Ramesh Gautam
Keyword(s):  
Resource Assessment ◽  
Groundwater Potential ◽  
Groundwater Resource ◽  
Sediment Distribution ◽  
North East ◽  
Hydrogeological Study ◽  
Potential Map ◽  
Groundwater Potential Map ◽  
Groundwater Resource Assessment ◽  
Dug Well

This study is carried out in parts of Surkhet valley, which is one of the Dun valleys (Inner Terai) in Nepal. Tubewell data was collected, dug well inventory with water table measurement was carried out followed by the data analysis leading to the groundwater resource assessment of the study area. The subsurface sediment distribution in the study area consist clay, sand and gravel giving rise to multiple aquifer horizons. Groundwater potential map has been prepared for parts of Surkhet valley and groundwater resource assessment has been carried out for the entire valley. Groundwater potential map was prepared using various thematic layers. Weights and rank were assigned, respectively to each thematic layer and its classes based on their significance for the groundwater occurrence. Most of the study area has medium groundwater potential with low potential at north east and high potential at southeast of the study area. The groundwater resource assessment for the valley, carried out by water balance method and aquifer analysis reveals that there is good groundwater reserve in the valley that can significantly fulfill the water demand in the area if properly exploited with required management of land and water resources in the area.

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