Sk Ziaul

Bio: Sk Ziaul is an academic researcher from University of Gour Banga. The author has contributed to research in topics: Urban heat island & Wetland. The author has an hindex of 8, co-authored 11 publications receiving 343 citations.

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

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.

Estimating wetland insecurity index for Chatra wetland adjacent English Bazar Municipality of West Bengal

Abstract: Charta wetland adjacent to the English Bazar Municipality is considered as the kidney of this urban area. In last 25 years, 43% of total wetland area is urbanized and this trend still continues. This paper aims to compute wetland insecurity index based on 11 selected natural (five) and man induced parameters (six) at multi-temporal scale (1990, 2010 and 2017). Weighted linear combination method is used for preparing wetland insecurity indices (natural, man-induced and integrated parameters specific) in Arc GIS (v-9.3) environment. Result clearly exhibited that man-induced parameters are more crucial for bringing greater north eastern wetland fringe area under high insecurity. Highly insecure wetland covers 6.6% to total area in case of man-induced parameters based spatial model is recorded in 2017 superseded previous phases. In case of natural parameter centric wetland insecurity model, no such highly insecure zone is found. Total 20.17, 33.23 and 72.18 ha areas are appeared as highly insecure wetland area for integrated wetland insecurity models for 1990, 2010 and 2017 respectively indicating increasing spatial extent. Built up area, population density, sedimentation seasonal drying out of the parts of wetland are appeared as major reason behind growing wetland insecurity in Chatra wetland.

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Abstract: Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images

Abstract: The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning.