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Journal ArticleDOI: 10.1007/S11356-021-12806-Z

Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh

02 Mar 2021-Environmental Science and Pollution Research (Springer)-Vol. 28, Iss: 26, pp 34450-34471
Abstract: Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.

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Topics: Wetland (54%)
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Journal ArticleDOI: 10.1007/S11356-021-14123-X
Abstract: Evaluation of the importance of ecosystem services (ES) of various wetlands is well reported with global and regional level research, but the degree to which spatial-temporal variations in water richness (availability of water) have had an effect on ES has not yet been examined. The present work is intended to investigate the influence of wetland fragmentation due to damming on wetland water richness and the impact of changes in water richness on the ecosystem service value (ESV) of the wetland-dominated rivers of the lower Punarbhaba Basin, India, and Bangladesh, as the case. Water richness models of pre- and post-dam periods have been constructed based on four hydro-ecological parameters (hydro-period, depth of water, consistency of water appearance, and wetland size) following the semi-quantitative analytic hierarchy process (AHP). ESV of different wetland types, with and without considering water richness effect, has been computed. The result indicates that the overall wetland area decreased from 73,563 to 52,123 km2 during the post-dam period. Approximately 53.8% of the high water-rich region is decreased. Total wetland ESV has been lowered by 63.4% from 1989 to 2019, with an average reduction rate of 2%. This is mainly due to the squeezing of the wetland area during the post-dam period. If the impact of water richness on ESV is considered, the scenario is found to be very distinct. Total ESV of various ESV areas amounted to $33 million during the pre-dam period and is reduced to $19.71 million during the post-dam period. If compared to the total ESV of the wetland without considering the effect of water richness, the calculated ESV gap was $105 million in pre-dam and $38 million in post-dam period indicating a widening of the gap. Maintaining the ES of wetland hydrological management, specifically the flow maintenance of river and riparian wetlands, is essential.

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Topics: Wetland (56%), Species richness (52%)

4 Citations


Journal ArticleDOI: 10.1007/S11356-021-17064-7
Abstract: Data-driven models are important to predict groundwater quality which is controlling human health. The water quality index (WQI) has been developed based on the physicochemical parameters of water samples. In this area, water quality is medium to poor and is found in saline zones; very high pH ranges are directly affected on the water quality in this study area. Conventional WQI computation demands more time and is often observed with enormous errors during the calculation of sub-indices. In the present work, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique. The groundwater samples were collected from the Akot basin area, located in the Akola district, Maharashtra, in India. A total of nine different input combinations were developed in this study. The datasets were demarcated into two classes (ratio 80:20) for model construction (training dataset) and model verification (testing dataset) using a fivefold cross-validation approach. The models were assessed using statistical and graphical appraisal metrics. The best input combinations varied among the model, generally, the optimal input variables (EC, pH, TDS, Ca, Mg, and Cl) during the training and validation stages. Results show that AR outperformed the other data-driven models (R2 = 0.9993, MAE = 0.5243, RMSE = 0.0.6356, %RAE = 3.8449, and RRSE% = 3.9925). The AR is proposed as an ideal model with satisfactory results due to enhanced prediction precision with the minimum number of input parameters and can thus act as the reliable and precise method in the prediction of WQI at the Akot basin.

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2 Citations


Open accessJournal ArticleDOI: 10.1007/S11069-021-04722-9
10 Feb 2021-Natural Hazards
Abstract: Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average EEMD-ANN and EEMD-SVM hybrid models showed 802–2248% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories Eleven out of 21 input parameters were selected based on the random forest variable importance analysis The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world

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1 Citations


Open accessJournal ArticleDOI: 10.3390/RS13173380
26 Aug 2021-Remote Sensing
Abstract: Defining stream channels in a watershed is important for assessing freshwater habitat availability, complexity, and quality. However, mapping channels of small tributary streams becomes challenging due to frequent channel change and dense vegetation coverage. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to obtain a 3D Digital Surface Model (DSM) to estimate the total in-stream channel and channel width within grazed riparian pastures. We used two methods to predict the stream channel boundary: the Slope Gradient (SG) and Vertical Slope Position (VSP). As a comparison, the same methods were also applied using low-resolution DEM, obtained with traditional photogrammetry (coarse resolution) and two more LiDAR-derived DEMs with different resolution. When using the SG method, the higher-resolution, UAV-derived DEM provided the best agreement with the field-validated area followed by the high-resolution LiDAR DEM, with Mean Squared Errors (MSE) of 1.81 m and 1.91 m, respectively. The LiDAR DEM collected at low resolution was able to predict the stream channel with a MSE of 3.33 m. Finally, the coarse DEM did not perform accurately and the MSE obtained was 26.76 m. On the other hand, when the VSP method was used we found that low-resolution LiDAR DEM performed the best followed by high-resolution LiDAR, with MSE values of 9.70 and 11.45 m, respectively. The MSE for the UAV-derived DEM was 15.12 m and for the coarse DEM was 20.78 m. We found that the UAV-derived DEM could be used to identify steep bank which could be used for mapping the hydrogeomorphology of lower order streams. Therefore, UAVs could be applied to efficiently map small stream channels in order to monitor the dynamic changes occurring in these ecosystems at a local scale. However, the VSP method should be used to map stream channels in small watersheds when high resolution DEM data is not available.

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Topics: Lidar (52%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/RS13193878
28 Sep 2021-Remote Sensing
Abstract: The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

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Topics: Wetland classification (59%), Ground truth (51%)

1 Citations


References
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143 results found


Open accessJournal ArticleDOI: 10.1023/A:1010933404324
Leo Breiman1Institutions (1)
01 Oct 2001-
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

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Topics: Random forest (63%), Multivariate random variable (57%), Random subspace method (57%) ... read more

58,232 Citations


Open accessJournal ArticleDOI: 10.1023/A:1022627411411
Corinna Cortes1, Vladimir Vapnik1Institutions (1)
15 Sep 1995-Machine Learning
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

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Topics: Feature learning (63%), Active learning (machine learning) (62%), Feature vector (62%) ... read more

35,157 Citations


Open accessJournal ArticleDOI: 10.1023/A:1022643204877
25 Mar 1986-Machine Learning
Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.

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Topics: Intelligent decision support system (57%), Influence diagram (56%), Decision engineering (55%) ... read more

16,062 Citations


Open access
01 Jan 2007-
Abstract: Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.

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Topics: Random forest (69%), Overfitting (52%), Ensemble learning (52%) ... read more

12,765 Citations


Open accessBook
01 Dec 1995-
Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.

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5,323 Citations