Supervised Classification of Groundwater Potential Mapping Using Integrated Machine Learning and GIS-Based Techniques

Shlash, Marwah Abdullah; Obead, Imad Habeeb

doi:10.18280/mmep.100313

Cited by 3 publications

(1 citation statement)

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“…The object is classified to find groups from a set of points, patterns, or entities, to get more in-depth information about the data, to look for similarities, and to map objects to certain groups [4]. Clustering is a process to obtain results from partitions in a data set [5], which are mapped into a group based on their level of similarity [6]. Hierarchical grouping uses a hierarchical (level) arrangement model in the data set based on the characteristics of the data [7], which are then grouped into a cluster [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Sumaryanti,

Nurcholis,

Muchlis

2024

MMEP

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Merauke Regency is the largest area in Papua Province and includes potential in the livestock sector. Regional potential analysis based on leading livestock population aims to provide regional information based on livestock sector potential, which can be used as information in policy making in government programs. A hybrid algorithm combining LQ and complete linkage can map potential livestock areas based on leading populations. The results of the LQ analysis show that there are six leading types of livestock: cows, buffaloes, horses, kampong chickens, laying chickens, and ducks. The leading livestock types can be used as a source of information regarding regional potential in the livestock business and classified into four clusters. The clustering of regional potential using a complete linkage hierarchical algorithm with a livestock population dataset by conducting four trials and yielding information that Semangga and Tanah Miring sub-districts have potential in the livestock sector. The proposed method used a hybrid approach to analyze the potential of livestock areas in Merauke and determine the leading types of livestock in the area to classify areas in each cluster and map the potential of livestock areas using GIS techniques.

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Section: Introductionmentioning

confidence: 99%

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Sumaryanti,

Nurcholis,

Muchlis

2024

MMEP

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Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors

Zhao,

Fan,

Arabameri

et al. 2024

Advances in Space Research

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

Baduna Koçyiğit,

Akay

2024

Water

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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.

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Supervised Classification of Groundwater Potential Mapping Using Integrated Machine Learning and GIS-Based Techniques

Cited by 3 publications

References 0 publications

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm

Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors

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

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