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The accurate modeling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms—Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production—and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability p of landslide occurrence decreases nearly exponentially with the distance x to the next road, fault, or river. Specifically, the results indicated that \(p \approx \exp \left( { - \lambda x} \right)\) where the length scale λ is about \(0.0797\) km−1 for road, \(0.108\) km−1 for fault, and \(0.734\) km−1 0.734 km−1 for river. Furthermore, according to the results, p follows, approximately, a lognormal function of elevation, while the equation \(p = p_{0} - K\left( {\theta - \theta_{0} } \right)^{2}\) fits well the dependence of landslide modeling on the slope-angle θ, with \(p_{0} \approx 0.64,\;\theta_{0} \approx 25.6^{ \circ } \;{\text{and}}\;\left| K \right| \approx 6.6 \times 10^{ - 4}\). However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modeling of landslide risk, as well as for priority planning in landslide risk management.
Elham Rafiei Sardooi; Ali Azareh; Tayyebeh Mesbahzadeh; Farshad Soleimani Sardoo; Eric J. R. Parteli; Biswajeet Pradhan. A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran. Environmental Earth Sciences 2021, 80, 1 -25.
AMA StyleElham Rafiei Sardooi, Ali Azareh, Tayyebeh Mesbahzadeh, Farshad Soleimani Sardoo, Eric J. R. Parteli, Biswajeet Pradhan. A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran. Environmental Earth Sciences. 2021; 80 (15):1-25.
Chicago/Turabian StyleElham Rafiei Sardooi; Ali Azareh; Tayyebeh Mesbahzadeh; Farshad Soleimani Sardoo; Eric J. R. Parteli; Biswajeet Pradhan. 2021. "A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran." Environmental Earth Sciences 80, no. 15: 1-25.
The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.
Rabin Chakrabortty; Subodh Chandra Pal; Manoranjan Ghosh; Alireza Arabameri; Asish Saha; Paramita Roy; Biswajeet Pradhan; Ayan Mondal; Phuong Thao Thi Ngo; Indrajit Chowdhuri; Ali P. Yunus; Mehebub Sahana; Sadhan Malik; Biswajit Das. Weather indicators and improving air quality in association with COVID-19 pandemic in India. Soft Computing 2021, 1 -22.
AMA StyleRabin Chakrabortty, Subodh Chandra Pal, Manoranjan Ghosh, Alireza Arabameri, Asish Saha, Paramita Roy, Biswajeet Pradhan, Ayan Mondal, Phuong Thao Thi Ngo, Indrajit Chowdhuri, Ali P. Yunus, Mehebub Sahana, Sadhan Malik, Biswajit Das. Weather indicators and improving air quality in association with COVID-19 pandemic in India. Soft Computing. 2021; ():1-22.
Chicago/Turabian StyleRabin Chakrabortty; Subodh Chandra Pal; Manoranjan Ghosh; Alireza Arabameri; Asish Saha; Paramita Roy; Biswajeet Pradhan; Ayan Mondal; Phuong Thao Thi Ngo; Indrajit Chowdhuri; Ali P. Yunus; Mehebub Sahana; Sadhan Malik; Biswajit Das. 2021. "Weather indicators and improving air quality in association with COVID-19 pandemic in India." Soft Computing , no. : 1-22.
Healthy farming systems play a vital role in improving agricultural productivity and sustainable food production. The present study aimed to propose an efficient framework to evaluate ecologically viable and economically sound farming systems using a matrix-based analytic hierarchy process (AHP) and weighted linear combination method with geo-informatics tools. The proposed framework has been developed and tested in the Central Highlands of Sri Lanka. Results reveal that more than 50% of farming systems demonstrated moderate status in terms of ecological and economic aspects. However, two vulnerable farming systems on the western slopes of the Central Highlands, named WL1a and WM1a, were identified as very poor status. These farming systems should be a top priority for restoration planning and soil conservation to prevent further deterioration. Findings indicate that a combination of ecologically viable (nine indicators) and economical sound (four indicators) criteria are a practical method to scrutinize farming systems and decision making on soil conservation and sustainable land management. In addition, this research introduces a novel approach to delineate the farming systems based on agro-ecological regions and cropping areas using geo-informatics technology. This framework and methodology can be employed to evaluate the farming systems of other parts of the country and elsewhere to identify ecologically viable and economically sound farming systems concerning soil erosion hazards. The proposed approach addresses a new dimension of the decision-making process by evaluating the farming systems relating to soil erosion hazards and suggests introducing policies on priority-based planning for conservation with low-cost strategies for sustainable land management.
Sumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. Proposing an ecologically viable and economically sound farming system using a matrix-based geo-informatics approach. Science of The Total Environment 2021, 794, 148788 .
AMA StyleSumudu Senanayake, Biswajeet Pradhan, Alfredo Huete, Jane Brennan. Proposing an ecologically viable and economically sound farming system using a matrix-based geo-informatics approach. Science of The Total Environment. 2021; 794 ():148788.
Chicago/Turabian StyleSumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. 2021. "Proposing an ecologically viable and economically sound farming system using a matrix-based geo-informatics approach." Science of The Total Environment 794, no. : 148788.
The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSO-ELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with p-value = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.
Hong Zhang; Hoang Nguyen; Xuan-Nam Bui; Biswajeet Pradhan; Ngoc-Luan Mai; Diep-Anh Vu. Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms. Resources Policy 2021, 73, 102195 .
AMA StyleHong Zhang, Hoang Nguyen, Xuan-Nam Bui, Biswajeet Pradhan, Ngoc-Luan Mai, Diep-Anh Vu. Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms. Resources Policy. 2021; 73 ():102195.
Chicago/Turabian StyleHong Zhang; Hoang Nguyen; Xuan-Nam Bui; Biswajeet Pradhan; Ngoc-Luan Mai; Diep-Anh Vu. 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms." Resources Policy 73, no. : 102195.
The selection of a suitable discretization method (DM) to discretize spatially continuous variables (SCVs) is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate (ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate (ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest (training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China. The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small (<1%), whereas the ICR values are obviously more in line with general recognition (usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the ML-based natural hazard susceptibility assessments.
Xianzhe Tang; Takashi Machimura; Wei Liu; Jiufeng Li; Haoyuan Hong. A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest. Geoscience Frontiers 2021, 12, 101253 .
AMA StyleXianzhe Tang, Takashi Machimura, Wei Liu, Jiufeng Li, Haoyuan Hong. A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest. Geoscience Frontiers. 2021; 12 (6):101253.
Chicago/Turabian StyleXianzhe Tang; Takashi Machimura; Wei Liu; Jiufeng Li; Haoyuan Hong. 2021. "A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest." Geoscience Frontiers 12, no. 6: 101253.
Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas.
Meriame Mohajane; Romulus Costache; Firoozeh Karimi; Quoc Bao Pham; Ali Essahlaoui; Hoang Nguyen; Giovanni Laneve; Fatiha Oudija. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators 2021, 129, 107869 .
AMA StyleMeriame Mohajane, Romulus Costache, Firoozeh Karimi, Quoc Bao Pham, Ali Essahlaoui, Hoang Nguyen, Giovanni Laneve, Fatiha Oudija. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators. 2021; 129 ():107869.
Chicago/Turabian StyleMeriame Mohajane; Romulus Costache; Firoozeh Karimi; Quoc Bao Pham; Ali Essahlaoui; Hoang Nguyen; Giovanni Laneve; Fatiha Oudija. 2021. "Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area." Ecological Indicators 129, no. : 107869.
Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain.
Sahar S. Matin; Biswajeet Pradhan. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review. Geocarto International 2021, 1 -27.
AMA StyleSahar S. Matin, Biswajeet Pradhan. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review. Geocarto International. 2021; ():1-27.
Chicago/Turabian StyleSahar S. Matin; Biswajeet Pradhan. 2021. "Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review." Geocarto International , no. : 1-27.
A semi-confined aquifer from Kirkuk Governorate, northern Iraq was taken as a case study to map groundwater potential in terms of both the availability and quality of the resource. In terms of quantity, five machine learning (ML) algorithms were used to model the relationship between locations of 1031 wells with specific-capacity data and nine influential groundwater occurrence factors. The algorithms used were linear discriminant analysis, classification and regression trees, linear vector quantization, random forest, and K-nearest neighbor. The groundwater occurrence factors used were elevation, slope, curvature, aspect, aquifer transmissivity, specific storage, soil, geology, and groundwater depth. Analysis of the worthiness of the factors used in the analysis by the information gain ratio indicated that five out of nine factors were worthy (average merit > 0): groundwater depth, elevation, transmissivity, specific storage, and soil. The remaining factors were non-worthy (average merit = 0) and thus they were removed from the analysis. The performance of the five ML algorithms was investigated using accuracy and kappa as evaluation metrics. Applying the models in the carte package of R software indicated that random forest was the best model. The probability values of this model were used for mapping quantitative groundwater potential after classification into three zones: poor, moderate, and excellent. Groundwater quality for drinking was modeled using the water quality index and the weights of the chemical constituents used (pH, TDS, Ca2+, Mg2+, Na+, \({\mathrm{SO}}_{4}^{2-}\), \({\mathrm{Cl}}^{-}\), and \({\mathrm{NO}}_{3}^{-}\)) were assigned using entropy information theory. A map of the groundwater quality index revealed five classes: < 50 (excellent), 50–100 (good), 100–150 (moderate), 150–200 (poor), and > 200 (extremely poor). Combining the groundwater quality index map with the groundwater potential map using summation operators revealed three zones of groundwater potential: poor, moderate, and excellent. Comparing this combined map with the quantitative groundwater potential map showed different patterns for the distribution of potential classes, which confirms that analysis of the groundwater potential should include groundwater quality as an important factor.
Alaa M. Al-Abadi; Alan E. Fryar; Arjan A. Rasheed; Biswajeet Pradhan. Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq. Environmental Earth Sciences 2021, 80, 1 -22.
AMA StyleAlaa M. Al-Abadi, Alan E. Fryar, Arjan A. Rasheed, Biswajeet Pradhan. Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq. Environmental Earth Sciences. 2021; 80 (12):1-22.
Chicago/Turabian StyleAlaa M. Al-Abadi; Alan E. Fryar; Arjan A. Rasheed; Biswajeet Pradhan. 2021. "Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq." Environmental Earth Sciences 80, no. 12: 1-22.
Recently, floods are occurring more frequently every year around the world due to increased anthropogenic activities and climate change. There is a need to develop accurate models for flood susceptibility prediction and mapping, which can be helpful in developing more efficient flood management plans. In this study, the Partial Decision Tree (PART) classifier and the AdaBoost, Bagging, Dagging, and Random Subspace ensembles learning techniques were combined to develop novel GIS-based ensemble computational models (ABPART, BPART, DPART and RSSPART) for flood susceptibility mapping in the Quang Binh Province, Vietnam. In total, 351 flood locations were used in the model study. This data was divided into a 70:30 ratio for model training (70% ≅ 255 locations) and (30% ≅ 96 locations) for model validation. Ten flood influencing factors, namely elevation, slope, curvature, flow direction, flow accumulation, river density, distance from river, rainfall, land-use, and geology, were used for the development of models. The OneR feature selection method was used to select and prioritize important factors for the spatial modeling. The results revealed that land-use, geology, and slope are the most important conditioning factors in the occurrence of floods in the study area. Standard statistical methods, including the ROC curve (AUC), were used for the performance evaluation of models. Results indicated that the performance of all models was good (AUC > 0.9) and RSSPART (AUC = 0.959) outperformed the others. Thus, the RSSPART model can be used for accurately predicting and mapping flood susceptibility.
Chinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology 2021, 599, 126500 .
AMA StyleChinh Luu, Binh Thai Pham, Tran Van Phong, Romulus Costache, Huu Duy Nguyen, Mahdis Amiri, Quynh Duy Bui, Luan Thanh Nguyen, Hiep Van Le, Indra Prakash, Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology. 2021; 599 ():126500.
Chicago/Turabian StyleChinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. 2021. "GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam." Journal of Hydrology 599, no. : 126500.
Land subsidence (LS) is significant problem that can lead to casualties, destruction of infrastructure, and socio-economic and environmental problems. In this study, we examine the Damghan Plain of Iran where LS poses a major obstacle to growth and management of the region. Dagging and random subspace (RSS) as meta- or ensemble-classifiers of a radial basis function neural network (RBFnn) were combined into two novel-ensemble intelligence approaches (Dagging-RBFnn and RSS-RBFnn) to predict and map the susceptibility of land units to subsidence. The goodness-of-fit (of training data) and prediction accuracy (of testing data) for the ensemble models were contrasted with the RBFnn, which is used as the benchmark for improvement. Details of 120 LS locations were examined and the data for twelve LS conditioning factors (LSCFs) were compiled. The LS points were randomly divided into four groups or folds, each comprised of 25 percent of the cases. The novel ensemble models were constructed using 75 percent (3 folds) and tested with the remaining 25 percent (onefold) in a four-fold cross-validation (CV) mechanism. Information-gain ratio and multicollinearity tests were used to select the LSCFs that would be used to estimate LS probabilities. The importance of each factor was calculated using a random forest (RF) model. The most important LSCFs were groundwater drawdown, land uses and land covers, elevation, and lithology. Twelve land subsidence susceptibility maps were generated using the k-fold CV approaches as each of the three models (RBFnn, Dagging-RBFnn and RSS-RBFnn) was applied to each of the four folds. The LS susceptibility models reveal a strong probability for LS on 15% to 24% of the plain. All of the maps generated achieved adequate levels of prediction accuracies and goodness-of-fits. The Dagging-RBFnn ensemble yielded the most robust maps, however. The ensemble of Dagging-RBFnn enhances the accuracy of modeling but the opposite condition was found for the RSS-RBFnn ensemble. It is evident that ensembles with meta classifiers might not always increase the accuracy of the base classifier. Overall, the southern part of the plain shows the highest LS risk. The results of this study suggests that groundwater withdrawal levels should be tracked and possibly restricted in regions with higher (extreme or moderate) probabilities of LS. This demonstrates that new approaches can support land use planning and decision making to minimize LS and improve sustainability.
Alireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment 2021, 1 -23.
AMA StyleAlireza Arabameri, M. Santosh, Fatemeh Rezaie, Sunil Saha, Romulus Coastache, Jagabandhu Roy, Kaustuv Mukherjee, John Tiefenbacher, Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment. 2021; ():1-23.
Chicago/Turabian StyleAlireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. 2021. "Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling." Stochastic Environmental Research and Risk Assessment , no. : 1-23.
Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta.
Dang An Tran; Maki Tsujimura; Nam Thang Ha; Van Tam Nguyen; Doan Van Binh; Thanh Duc Dang; Quang-Van Doan; Dieu Tien Bui; Trieu Anh Ngoc; Le Vo Phu; Pham Thi Bich Thuc; Tien Dat Pham. Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators 2021, 127, 107790 .
AMA StyleDang An Tran, Maki Tsujimura, Nam Thang Ha, Van Tam Nguyen, Doan Van Binh, Thanh Duc Dang, Quang-Van Doan, Dieu Tien Bui, Trieu Anh Ngoc, Le Vo Phu, Pham Thi Bich Thuc, Tien Dat Pham. Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators. 2021; 127 ():107790.
Chicago/Turabian StyleDang An Tran; Maki Tsujimura; Nam Thang Ha; Van Tam Nguyen; Doan Van Binh; Thanh Duc Dang; Quang-Van Doan; Dieu Tien Bui; Trieu Anh Ngoc; Le Vo Phu; Pham Thi Bich Thuc; Tien Dat Pham. 2021. "Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam." Ecological Indicators 127, no. : 107790.
In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
Hamid Darabi; Omid Rahmati; Seyed Amir Naghibi; Farnoush Mohammadi; Ebrahim Ahmadisharaf; Zahra Kalantari; Ali Torabi Haghighi; Seyed Masoud Soleimanpour; John P. Tiefenbacher; Dieu Tien Bui. Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood. Geocarto International 2021, 1 -27.
AMA StyleHamid Darabi, Omid Rahmati, Seyed Amir Naghibi, Farnoush Mohammadi, Ebrahim Ahmadisharaf, Zahra Kalantari, Ali Torabi Haghighi, Seyed Masoud Soleimanpour, John P. Tiefenbacher, Dieu Tien Bui. Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood. Geocarto International. 2021; ():1-27.
Chicago/Turabian StyleHamid Darabi; Omid Rahmati; Seyed Amir Naghibi; Farnoush Mohammadi; Ebrahim Ahmadisharaf; Zahra Kalantari; Ali Torabi Haghighi; Seyed Masoud Soleimanpour; John P. Tiefenbacher; Dieu Tien Bui. 2021. "Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood." Geocarto International , no. : 1-27.
Limited hydrogeological data accessibility leads scholars to improve the robustness of present qualitative groundwater vulnerability assessment methods using mathematical techniques. In the present study, we implemented three GIS-based groundwater vulnerability assessment indices, namely DRASTIC (Depth to water table, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), SINTACS (Soggicenza, Infiltrazione, Non saturo, Tipologia della copertura, Acquifero, Conducibilità, and Superficie topografica), and GODS (Groundwater confinement, Overlying strata, Depth to groundwater, and Soil media) to assess the groundwater vulnerability levels. Although DRASTIC results showed better performance with respect to the nitrate concentration data from 50 observation wells in the study site, the index is still unreliable due to its inherent drawbacks, including subjectivity. Hybrid PSO-GA method is a successful optimization algorithm gathering the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) while avoiding their shortcomings. The DRASTIC weighting system is optimized using PSO-GA optimization algorithm. Also, Step-wise Weight Assessment Ratio Analysis (SWARA) as a Multi-Attribute Decision Making (MADM) method is applied for changing ranges of DRASTIC rates and weights. The vulnerability indices obtained from SWARA-SWARA, DRASTIC-PSO-GA, and SWARA-PSO-GA frameworks are evaluated and compared with generic DRASTIC regarding the nitrate concentration dataset by employing Area Under the ROC Curve (AUC) and Grey relational analysis methods. Results show a noticeable improvement of correlation between indices and observed nitrate concentration after modifications and optimizations. The new hybrid SWARA-PSO-GA framework is the most effective framework in assessing the vulnerability of the present study area.
Maryam Torkashvand; Aminreza Neshat; Saman Javadi; Biswajeet Pradhan. New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method. Journal of Hydrology 2021, 598, 126446 .
AMA StyleMaryam Torkashvand, Aminreza Neshat, Saman Javadi, Biswajeet Pradhan. New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method. Journal of Hydrology. 2021; 598 ():126446.
Chicago/Turabian StyleMaryam Torkashvand; Aminreza Neshat; Saman Javadi; Biswajeet Pradhan. 2021. "New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method." Journal of Hydrology 598, no. : 126446.
Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.
Mahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies 2021, 35, 100825 .
AMA StyleMahdi Panahi, Khabat Khosravi, Sajjad Ahmad, Somayeh Panahi, Salim Heddam, Assefa M Melesse, Ebrahim Omidvar, Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies. 2021; 35 ():100825.
Chicago/Turabian StyleMahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. 2021. "Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran." Journal of Hydrology: Regional Studies 35, no. : 100825.
Landslides are considered to be a significant risk to life and property all over the world in general and in Vietnam in particular. Spatial prediction of landslides is required to reduce the landslides risk and to plan the development of hilly areas. In this regard, the accurate landslide susceptibility maps are very useful tool for decision-makers to identify areas where new landslides are likely to occur for planning timely adequate remedial measures. For the development of landslide susceptibility maps, seven hybrid models were developed namely AdaBoost-LMT (ABLMT), Bagging-LMT (BLMT), Cascade Generalization-LMT (CGLMT), Dagging-LMT (DLMT), MultiBoostAB-LMT (MBLMT), Rotation Forest-LMT (RFLMT) and Random Sub Space-LMT (RSSLMT) with Logistic Model Trees (LMT) as a base classifier. The models performance and validation was assessed thourgh various statistical indices such as sensitivity, specificity, accuracy, Area Under ROC Curve, RMSE and k index. The results show that all these models are performing well for the prediction of landslide susceptibility in the study area, but the performance of the RSSLMT model is the best (Area Under the ROC Curve (AUC): 0.816). In this study open source data has been used for the development of landslide susceptibility maps Along National Highway-6, passing through Hoa Binh province, Vietnam. These approaches can be applied also in other hilly regions of the world which are susceptible to landslides for better landslides prevention and management.
Ha Thi Hang; Hoang Tung; Pham Duy Hoa; Nguyen Viet Phuong; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Hoang-Anh Le; Hiep Van Le; Indra Prakash; Binh Thai Pham. Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models. Geocarto International 2021, 1 -26.
AMA StyleHa Thi Hang, Hoang Tung, Pham Duy Hoa, Nguyen Viet Phuong, Tran Van Phong, Romulus Costache, Huu Duy Nguyen, Mahdis Amiri, Hoang-Anh Le, Hiep Van Le, Indra Prakash, Binh Thai Pham. Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models. Geocarto International. 2021; ():1-26.
Chicago/Turabian StyleHa Thi Hang; Hoang Tung; Pham Duy Hoa; Nguyen Viet Phuong; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Hoang-Anh Le; Hiep Van Le; Indra Prakash; Binh Thai Pham. 2021. "Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models." Geocarto International , no. : 1-26.
Wildfire is an environmental hazard that has both local and global effects, causing economic losses and various severe environmental problems. Due to the adverse effects of climate changes and anthropogenic activities, wildfire is anticipated more frequent and extreme; therefore, new and more efficient tools for forest fire prevention and control are essential. This study proposes a new deep neural computing approach for spatial prediction of wildfire in a tropical climate area. For this purpose, deep neural computing (Deep-NC) with a structure of 3 hidden layers was proposed. The Rectified Linear Unit (ReLU) activation function was adopted to infer wildfire dangers from the input factors. To search and optimize the weights of the model, Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), and Adadelta optimizers were employed. Also, this study has established a Geographic Information System (GIS) database for Gia Lai province (Vietnam) to train and verify the newly developed deep computing approach. The twelve ignition factors, namely, slope, aspect, elevation, curvature, land use, NVDI, NDWI, NDMI, temperature, wind speed, relative humidity, and rainfall, have been used to characterize the study area with respect to forest fire susceptibility. According to experimental results, the Adam optimized Deep-NC model delivered the highest predictive accuracy (AUC = 0.894, Kappa = 0.63). Accordingly, this model has been employed to establish a forest fire susceptibility map for Gia Lai province. The proposed Deep-NC model and the newly constructed forest fire susceptibility map can help local authorities in land use planning and hazard mitigation/prevention.
Hung Van Le; Duc Anh Hoang; Chuyen Trung Tran; Phi Quoc Nguyen; Van Hai Thi Tran; Nhat Duc Hoang; Mahdis Amiri; Thao Phuong Thi Ngo; Ha Viet Nhu; Thong Van Hoang; Dieu Tien Bui. A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. Ecological Informatics 2021, 63, 101300 .
AMA StyleHung Van Le, Duc Anh Hoang, Chuyen Trung Tran, Phi Quoc Nguyen, Van Hai Thi Tran, Nhat Duc Hoang, Mahdis Amiri, Thao Phuong Thi Ngo, Ha Viet Nhu, Thong Van Hoang, Dieu Tien Bui. A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. Ecological Informatics. 2021; 63 ():101300.
Chicago/Turabian StyleHung Van Le; Duc Anh Hoang; Chuyen Trung Tran; Phi Quoc Nguyen; Van Hai Thi Tran; Nhat Duc Hoang; Mahdis Amiri; Thao Phuong Thi Ngo; Ha Viet Nhu; Thong Van Hoang; Dieu Tien Bui. 2021. "A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas." Ecological Informatics 63, no. : 101300.
The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.
Nguyen Binh; Huynh Nhut; Nguyen An; Tran Phuong; Nguyen Hanh; Giang Thao; The Pham; Pham Hong; Le Ha; Dieu Bui; Pham Hoa. Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine. ISPRS International Journal of Geo-Information 2021, 10, 226 .
AMA StyleNguyen Binh, Huynh Nhut, Nguyen An, Tran Phuong, Nguyen Hanh, Giang Thao, The Pham, Pham Hong, Le Ha, Dieu Bui, Pham Hoa. Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine. ISPRS International Journal of Geo-Information. 2021; 10 (4):226.
Chicago/Turabian StyleNguyen Binh; Huynh Nhut; Nguyen An; Tran Phuong; Nguyen Hanh; Giang Thao; The Pham; Pham Hong; Le Ha; Dieu Bui; Pham Hoa. 2021. "Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine." ISPRS International Journal of Geo-Information 10, no. 4: 226.
Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang County, in China, by integrating two independent models of frequency ratio and index of entropy with multilayer perceptron and classification and regression tree models. The flood locations of the study area were identified through the flood inventory process, and 12 flood conditioning factors were used in the training and validation processes. According to the results of the linear support vector machine, elevation, slope angle, and soil have the highest predictive ability. The experimental results of the four hybrid models demonstrate that between 20% and 50% of the study area has high and very high flood susceptibility. The multilayer perceptron-probability density hybrid model is the most effective among the six comparative methods.
Yi Wang; Zhice Fang; Haoyuan Hong; Romulus Costache; Xianzhe Tang. Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management 2021, 289, 112449 .
AMA StyleYi Wang, Zhice Fang, Haoyuan Hong, Romulus Costache, Xianzhe Tang. Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management. 2021; 289 ():112449.
Chicago/Turabian StyleYi Wang; Zhice Fang; Haoyuan Hong; Romulus Costache; Xianzhe Tang. 2021. "Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree." Journal of Environmental Management 289, no. : 112449.
A new hybrid model approach based on Imperialist Competitive Algorithm, a socio-politically optimization, and neural computing networks (ICA-NeuralNet) was developed and proposed in this study with the aim is to improve the quality of the shallow landslide susceptibility assessment at the Ha Long city area, Quang Ninh province. This area, which belongs to one of the three key economic regions of Vietnam, has a high urbanization speed during the last ten years. However, the landslide has been a significant environmental hazard problem during the last five years due to extreme torrential rainstorms. For this regard, a geographic information system (GIS) database was established, which contains 170 landslide polygons that occurred during the last five years and ten influencing factors. The database was used for training and validating the ICA-NeuralNet model. The results showed that the integrated model achieves high performance with classification accuracy rates of 82.4% on the training dataset and 78.2% on the testing dataset. Therefore, the ICA-NeuralNet is subsequently employed for generating a landslide susceptibility map of the study area, which greatly supports the land-use planning as well as hazard mitigation/prevention of local authority.
Viet-Ha Nhu; Nhat-Duc Hoang; Mahdis Amiri; Tinh Thanh Bui; Phuong Thao T. Ngo; Pham Viet Hoa; Pijush Samui; Long Nguyen Thanh; Tu Pham Quang; Dieu Tien Bui. An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas. Environmental Earth Sciences 2021, 80, 1 -18.
AMA StyleViet-Ha Nhu, Nhat-Duc Hoang, Mahdis Amiri, Tinh Thanh Bui, Phuong Thao T. Ngo, Pham Viet Hoa, Pijush Samui, Long Nguyen Thanh, Tu Pham Quang, Dieu Tien Bui. An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas. Environmental Earth Sciences. 2021; 80 (7):1-18.
Chicago/Turabian StyleViet-Ha Nhu; Nhat-Duc Hoang; Mahdis Amiri; Tinh Thanh Bui; Phuong Thao T. Ngo; Pham Viet Hoa; Pijush Samui; Long Nguyen Thanh; Tu Pham Quang; Dieu Tien Bui. 2021. "An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas." Environmental Earth Sciences 80, no. 7: 1-18.
Road networks are one of the main urban features. Therefore, road parts extraction from high-resolution remotely sensed imagery and updated road database are beneficial for many GIS applications. However, owing to the presence of various types of obstacles in the images, such as shadows, cars, and trees, with similar transparency and spectral values as road class, achieving accurate road extraction using different classification and segmentation methods is still difficult. This paper proposes an integrated method combining segmentation and classification methods with connected components analysis to extract road class from orthophoto images. The proposed technique is threefold. First, multiresolution segmentation method was applied to segment images. Then, the main classification methods, namely, decision trees (DT), k-nearest neighbors (KNN), and support vector machines (SVM), were implemented based on spectral, geometric, and textural information to classify the obtained results into two classes: road and non-road. Three main accuracy evaluation measures, such as recall, precision, and F1-score, were evaluated to determine the performance of the proposed method, with respective average values of 87.62%, 89.71%, and 88.61%, respectively, for DT; 86.61%, 88.17%, and 87.30%, respectively, for KNN; and 89.83%, 89.52%, and 89.67%, respectively, for SVM. Finally, connected components labelling was used to extract road component parts, and morphological operation was employed to delete non-road parts and noises and improve the performance. These results were also compared with other prior works, which confirmed that the integrated method is an effective road extraction technique.
Abolfazl Abdollahi; Biswajeet Pradhan. Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images. Expert Systems with Applications 2021, 176, 114908 .
AMA StyleAbolfazl Abdollahi, Biswajeet Pradhan. Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images. Expert Systems with Applications. 2021; 176 ():114908.
Chicago/Turabian StyleAbolfazl Abdollahi; Biswajeet Pradhan. 2021. "Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images." Expert Systems with Applications 176, no. : 114908.