Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm

Masroor, Md; Rehman, Sufia; Sajjad, Haroon; Rahaman, Md Hibjur; Sahana, Mehebub; Ahmed, Rizwan Raheem; Singh, Roshani

doi:10.1016/j.gsd.2021.100554

Cited by 53 publications

(5 citation statements)

References 58 publications

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“…A popular implementation of RF refers to the predictive modeling of several groundwater pollution sources, which is crucial in areas with significant agricultural or urban growth, especially in arid and semi-arid climates (e.g., Wheeler et al, 2015;Canion et al, 2019;Knoll et al, 2019;Messier et al, 2019;Ouedraogo et al, 2019;Lahjouj et al, 2020;Pham et al, 2021;He et al, 2022). Other noteworthy applications of RF are the prediction of groundwater levels (e.g., Wang et al, 2018;Saha et al, 2022), assessment of drought impact on groundwater potential (Masroor et al, 2021), classification of surface-groundwater interaction (Yang et al, 2019), identification of potential sites for groundwater artificial recharge (Norouzi and Shahmohammadi-Kalalagh, 2019;Naghibi et al, 2020), investigation of the spatial and temporal variations of soil salinity (Fathizad et al, 2020) and modeling of the shallow water table at high spatial resolution to calculate the risk for groundwater-induced flood events (Koch et al, 2019).…”

Section: Multi-fidelity Approach Of Seawater Intrusionmentioning

confidence: 99%

Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

et al. 2023

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In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.

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Section: Multi-fidelity Approach Of Seawater Intrusionmentioning

confidence: 99%

Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

et al. 2023

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“…Aspect is the direction in which a slope faces, and it impacts the physical properties of a slope such as lineament, and exposure to sunlight (Masroor et al 2021). DEM was used to construct aspect data (Fig.…”

Section: Aspectmentioning

confidence: 99%

Novel hybrid models to enhance the efficiency of groundwater potentiality model

et al. 2022

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The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

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“…Sustainable forest management involves optimizing the benefits of forest resources to satisfy the needs of society by conserving and maintaining forest ecosystems for both present and future generations. Several studies on the influence of climate change on forest cover have found that shifting patterns of vegetation cover varied across areas due to spatiotemporal changes in climate change and eco-environmental factors [100]. It is also evident that changes in forest dynamics are not only due to climate change but are also accelerated by several anthropogenic disturbances [18].…”

Section: Climate Change Impact and Forest Vulnerabilitymentioning

confidence: 99%

Forest Vulnerability to Climate Change: A Review for Future Research Framework

Roshani

Sahana

Kumar

et al. 2022

Forests

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Climate change has caused vulnerability not only to the forest ecosystem but also to forest-dependent communities. Therefore, its management is essential to increase forest ecosystem services and reduce vulnerability to climate change using an integrated approach. Although many scientific studies examined climate change impact on forest ecosystems, forest vulnerability assessment, including forest sensitivity, adaptability, sustainability and effective management was found to be scant in the existing literature. Through a systematic review from 1990 to 2019, this paper examined forest vulnerability to climate change and its management practices. In this paper, descriptive, mechanism and thematic analyses were carried out to analyze the state of existing research, in order to understand the concept of vulnerability arising from climate change and forest management issues. The present study proposed a framework for integrated forest assessment and management for addressing such issues in future research. The conversion of forest land into other land uses, forest fragmentation, forest disturbance and the effects of climate change on the forest ecosystem are the existing problems. Forest vulnerability, effective adaptation to forest ecosystems and long-term sustainability are priority areas for future research. This study also calls for undertaking researchers at a local scale to involve communities for the effective management of forest ecosystems.

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Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm

Cited by 53 publications

References 58 publications

Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

Novel hybrid models to enhance the efficiency of groundwater potentiality model

Forest Vulnerability to Climate Change: A Review for Future Research Framework

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