Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band, Shahab S.; Janizadeh, Saeid; Pal, Subodh Chandra; Saha, Asish; Chakrabortty, Rabin; Shokri, Manouchehr; Mosavi, Amir

doi:10.3390/s20195609

Cited by 133 publications

(68 citation statements)

References 70 publications

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“…A total of 12 geoenvironmental control factors have been used to meet our research objective. • Multi-collinearity testing was conducted among the conditioning factors used in this study using inflation factor variance (VIF) and tolerance (TOL) techniques (Band et al, 2020;Arabameri et al, 2021). • To map the LS susceptibility of a number of MLAs, i.e., MaxEnt, GLM, ANN, and SVM have been used in this study together with a total of eleven ensemble methods.…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Methodsmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…Soil erosion is the most widespread form of land degradation and the main source of water pollution worldwide (Arabameri et al 2020;Band et al 2020). About 85% of land degraded globally is due to soil erosion, causing a decline in crop yield up to 17% (Oldeman et al 1991) and lead to 10 million hectare of land abandonment each year (Faeth and Crosson 1994;Pimentel et al 1995), and cause many off-site problems such as reduction of the storage capacity of reservoirs at a rate of about 1% per year (Mahmood 1987).…”

Section: Introductionmentioning

confidence: 99%

GIS and remote sensing-based assessment of soil erosion risk using RUSLE model in South-Kivu province, eastern, Democratic Republic of Congo

Kulimushi

Choudhari

Mubalama

et al. 2021

Geomatics, Natural Hazards and Risk

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Soil erosion risk assessment in South-Kivu longs for the colonial epoch, while the province faces the problem of extreme degradation of land in the form of soil erosion. Thus, the study attempts to assess the soil erosion at the province level using the Revised Universal Soil Loss Equation (RUSLE) in conjunction with the Geographical Information System (GIS), and remote sensing data. The estimated total soil erosion was 2.084 million tons; with an annual average of 138.2 t ha À1 yr À1 . Moreover, the soil loss greater than 100 t ha À1 yr À1 accounts for 45.2% of the total erosive land. The soil erosion worsening nearly the entire territories range between 87 t ha À1 yr À1 in Shabunda to 248 t ha À1 yr À1 in Uvira. Under high aggressiveness of rainfall with mean of 1857.19 mm/y, the highest rate found in Perennial crop, Trees, and Cropland in contrast to Shrub and closed Forest was mainly due to the mean slope of 22% found in the former Land cover categories compared to 17% of Shrubland and closed forest. The adoption of terracing could reduce by 76% the current rate of cropland i.e., from (162.12 t ha À1 yr À1 to 38 t ha À1 yr À1 ). Therefore it is strongly recommended.

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“…In this research, the erosion potentiality in watershed scale has been estimated with the help of evidential belief function (EBF), spatial logistic regression (SLR) and ensemble of EBF and SLR. The ensemble approach is more optimistic regarding the perdition of different environmental hards that already established by different researchers (Arabameri et al, 2020;Band et al, 2020aBand et al, , 2020b, so we considered this ensemble approach for estimation of erosion potentiality.…”

Section: Approaches For Estimating Erosion Potentialitymentioning

confidence: 99%

Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

Chakrabortty

Pal

Arabameri

et al. 2021

Environ Dev Sustain

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The large-scale water-induced erosion is one of the most determining elements on land degradation in subtropical monsoon-dominated region. From this large-scale erosion, the fertility of the agricultural land has been decline consequently. So, estimation of the amount of erosion and its accurate prediction is necessary for escaping from this hazardous situation. In this study, the application of evidential belief function (EBF), spatial logistic regression (SLR) and ensemble of EBF and SLR to estimate the erosion potentiality with the help of ArcGIS and Soil and water assessment tool (SWAT). The average annual soil erosion has been estimated with the help of revised universal soil loss equation (RUSLE) and geographical information system (GIS). Apart from this to evaluate the importance of morphotectonic parameters on soil erosion, the correlation between erosion potentiality and average annual soil erosion has been quantified. In large-scale erosion, there is a direct impact of storm rainfall event in monsoon period in the entire subtropical region. Here, in erosion potentiality assessment, the optimal capacity of ensemble EBF-SLR is higher than the single alone methods, i.e., EBF and SLR. So, the mentioned approaches can be applied in soil erosion research in subtropical environment with considering the erosion causal parameters. This type of information can be helpful to the decision-maker and stakeholders to take proper initiative to reducing the rate of erosion. The main task of the future researcher is to implement this method more accurate ways with considering more reliable variables and slight modifications of the approaches in keeping in the view of regional environment.

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Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Cited by 133 publications

References 70 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

GIS and remote sensing-based assessment of soil erosion risk using RUSLE model in South-Kivu province, eastern, Democratic Republic of Congo

Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

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