Structure and evolution of the Ban Pong Basin, Chiang Mai Province, Thailand

Mankhemthong, Niti; Takaew, Pratchaya; Rhodes, Brady P.

doi:10.1016/j.jseaes.2018.09.010

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In addition to the mountainous landscape of Chiang Mai's westernmost region, the province boasts a variety of other diverse terrains i.e., dense forests, valleys, and rolling hills. This province is the northern region's largest intermontane basin, characterized by its location within the Chiang Mai Basin (Mankhemthong, 2019). The area is notable for its complex geological processes and formations.…”

Section: Study Areamentioning

confidence: 99%

“…To the best of our knowledge, previously, there has been no research related to the STK ensemble modelling of landslide susceptibility in our experimental area. The study will be conducted in Chiang Mai Province, Thailand, which is known for its intermontane basin topography and is highly susceptible to landslides, particularly in hilly areas (Wattananikorn et al, 1995;Mankhemthong, 2019). Almost 10% of the region's population lives in landslide-prone areas.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Intarat,

Yoomee,

Hussadin

et al. 2024

Environ. Nat. Resour. J.

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In mountainous terrain, landslides are common, particularly in intermontane basin locations. Such regions can adversely affect both human beings and the environment. In the assessment of landslide susceptibility, machine learning (ML) algorithms are increasingly popular due to their compatibility with geospatial data and tools. Herein, this study evaluated the performance of four ML algorithms: namely, random forest (RF), gradient boost (GB), extreme gradient boost (XGB), and stacking ensemble (STK). These algorithms were implemented to create a practical model of landslide susceptibility. The site under investigation is in the province of Chiang Mai, an intermontane basin area in northern Thailand where populations are settled. To address issues of multicollinearity, the variance inflation factor (VIF) was used. Eight out of fourteen factors were selected for examination; hyperparameters of each model were tested to acquire the best combination. Results indicated that the STK model outperforms all other models, providing evaluation metrics (precision, recall, F1-score, and overall accuracy) of 82.92%, 81.18%, 82.04%, and 81.75%, respectively. The area under the receiver operating characteristic (ROC) curve also reveals the high efficiency of the model, achieving 0.8928. However, further analysis of the appropriate model or base learner is necessary for achieving even higher predictive results.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Landslide Susceptibility in the Intermontane Basin Area of Northern Thailand

Intarat,

Yoomee,

Hussadin

et al. 2024

Environ. Nat. Resour. J.

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“…The Doi Inthanon and Doi Suthep are two metamorphic core complexes in northern Thailand ductilely deformed in the Oligocene followed by post-kinematic intrusions at 27 Ma (Dunning et al, 1995). The Chiang Mai Basin formed by the end of the Oligocene and available AFT ages indicate an exhumation and cooling to upper crustal levels between ~24 Ma and 18 Ma (Mankhemthong et al, 2018;Morley, 2009;Upton, 1999). Upton et al (1997), conducted a thermal modelling for one sample with sufficient track lengths of the Doi Inthanon complex, and estimated cooling rates between 8.5 °C / Ma and 25 °C / Ma corresponding to a denudation of 550-3500 m/Ma which is in accordance with our cooling rates through the APAZ in Khanom.…”

Section: A Northward Migration Of the Chain Of Eventsmentioning

confidence: 99%