Groundwater Level Prediction Using Support Vector Machine and M5 Model Tree-A Case Study

Radhakrishnan, R.; C.A., Lekshmy Devi

doi:10.2139/ssrn.4512253

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning (ML) algorithms outperform traditional approaches in groundwater potential zone modelling because of their ability to manage complex, non-linear interactions across varied datasets and adapt to changing hydrogeological circumstances. Unlike previous techniques, ML algorithms easily deal with non-linearity, making them crucial for mapping groundwater potential zones and improving decision-making in sustainable water resource management 17 – 19 . The most appropriate ML algorithm is determined by dataset features, and comparison studies are critical for identifying the method that works best in a certain environment.…”

Section: Introductionmentioning

confidence: 99%

“…The most appropriate ML algorithm is determined by dataset features, and comparison studies are critical for identifying the method that works best in a certain environment. Various machine learning algorithms such as the: artificial neural network (ANN) algorithm 20 , 21 ; function model 22 ; the decision tree 23 ; the greatest entropy and random forest (RF) models 24 ; and shannon entropy (SE) to Geographic Information Systems (GIS) 25 , decision tree 18 , random forest (RF) 26 , deep learning 27 , support vector machine learning model (SVM) 17 etc. have been used over the years to detect the groundwater potential zones.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices

Ghanbarzadeh,

Ghorbani,

Salarijazi

et al. 2024

Atmospheric Science Letters

View full text Add to dashboard Cite

In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year.

show abstract

Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models

Baduna Koçyiğit,

Akay

2024

Water

View full text Add to dashboard Cite

Identifying groundwater potential zones in a basin and developing a sustainable management plan is becoming more important, especially where surface water is scarce. The main aim of the study is to prepare the groundwater potential maps (GWPMs) considering the bivariate statistical models of frequency ratio (FR), weight of evidence (WoE), and the multi-criteria decision-making (MCDM) model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) hybridized with FR and WoE. Two distance measures, Euclidean and Manhattan, were used in TOPSIS to evaluate their effect on GWPMs. The research focused on the Burdur Lake catchment located in the southwest of Türkiye. In total, 74 wells with high yields were chosen randomly for the analysis, 52 (70%) for training, and 22 (30%) for testing processes. Sixteen groundwater conditioning factors were selected. The area under the receiver operating characteristic (AUROC) and true skill statistics (TSS) were utilized to examine the goodness-of-fit and prediction accuracy of approaches. The TOPSIS-WoE-Manhattan model and the FR and WoE models gave the best AUROC values of 0.915 and 0.944 for the training and testing processes, respectively. The best TSS values of 0.827 and 0.864 were obtained by the TOPSIS-FR-Euclidean and WoE models for the training and testing processes, respectively.

show abstract

Groundwater Level Prediction Using Support Vector Machine and M5 Model Tree-A Case Study

Cited by 4 publications

References 2 publications

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices

Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models

Contact Info

Product

Resources

About