How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

Saha, Tamal Kanti; Pal, Swades; Debanshi, Sandipta; Khatun, Rumki; Singha, Pankaj; Mandal, Indrajit

doi:10.1016/j.jenvman.2021.113344

Cited by 54 publications

(20 citation statements)

References 96 publications

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“…Accordingly, the primary objective of this study was to develop various ML models to identify and predict current and future flood susceptible areas while considering the spatial and temporal impacts of climate change on floods. From a spatial perspective, there are three crucial elements in efficiently mapping flood susceptibility: (1) selection of appropriate flood explanatory factors, (2) spatial resolution of the flood explanatory factors, and (3) the accuracy and efficiency of data layer integration models [ 97 ]. Even though there is no conventional technique for selecting the factors that would best predict future floods, we chose various sets of factors regarding the literature review [ 5 , 54 , 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although the effects of climate change are still debatable, the impacts of climatic variability require more investigation. While precipitation is recognized as the most significant climatic factor for flooding in some places [ 104 ] and the runoff factor in flood events [ 105 ], regarding many earlier studies, the most influential factors for flood events include elevation [ 26 , 52 ], slope [ 52 ], distance from rivers [ 26 , 52 , 106 , 107 ], drainage density, and land cover/land use [ 52 , 97 ]. Comparing the area percentages of flood susceptibility classes in both watersheds given their corresponding precipitation maps demonstrated that even though the Loup watershed receives significantly more precipitation than the Lower Nicola River watershed, the area percentages of moderate, high, and very high flood susceptibility classes in the Loup watershed were much trivial compared to the area percentages of the same classes in the Lower Nicola River watershed.…”

Section: Discussionmentioning

confidence: 99%

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Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Gharakhanlou

Pérez

2022

Entropy

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The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = −1.68%, Low = −5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = −1.61%, Low = +2.98%, Moderate = −3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = −0.38%, Low = −0.81%, Moderate = −0.95%, High = +1.72%, and Very High = +0.42% and Very Low = −1.31%, Low = −1.35%, Moderate = −1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Gharakhanlou

Pérez

2022

Entropy

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“…This index is an indicator of availability of water in an area as a result of topographic effects on water accumulation (Mokarram et al 2015). This index represents the amount of water contained in the region at each pixel scale (Saha et al 2021) and is calculated using Eq. ( 1):…”

Section: Twimentioning

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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“…It depicts how topography influences runoff generation and the volume of flow that accumulates in a basin. This index depicts the volume of water stored in the area at each pixel scale [93] and is measured using Equation (3).…”

Section: Topographic Wetness Indexmentioning

confidence: 99%

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Mallick

Talukdar

Kahla

et al. 2021

Water

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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.

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How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

Cited by 54 publications

References 96 publications

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Novel hybrid models to enhance the efficiency of groundwater potentiality model

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

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