Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India

Landslides are one of the most widespread natural disasters and pose severe threats to people, properties, and the environment in many areas. Landslide susceptibility mapping (LSM) has proven useful in designing landslide mitigation strategies for reducing disaster risk and societal and economic losses, which are essential for land use planning, hazard prevention, and risk management. Recent efforts for improving accuracies of LSM have focused on the utilization of convolutional neural network (CNN) in some image-related tasks, however, due to the inconsistency of data representation, CNN-related studies need to be further explored. In this study, a CNN-based approach for LSM was proposed and experimentally applied in a Jiuzhaigou region where a catastrophic earthquake taken place on 8 August 2017, in Sichuan, China. To address the issue of data representation in the CNN model, we proposed a multiscale sampling strategy which to our knowledge is novel in LSM. In this way, the multiscale training samples (i.e., small scale, medium scale and large scale) were generated from the selected eleven landslide causative factors. The success-rate curve (SRC) and prediction-rate curve (PRC) were applied to validate the LSM results, and three conventional machine learning algorithms, i.e., logistic regression, multi-layer perceptron (MLP) neural network and radial basis function (RBF) neural network, were attempted for comparison. Specifically, MLP neural network achieved the best performance among three machine learning methods, with the area under the SRC (AU-SRC) and PRC (AU-PRC) values of 81.18% and 82.84%, respectively. Nevertheless, the AU-SRC and AU-PRC values of CNN-based approach reached to 97.45% and 88.02%, which were about 16% and 6% higher than that of the MLP neural network, respectively. The present study demonstrated both the excellent goodness-of-fit and strong prediction ability of CNN-based approach for LSM, which also showed the effectiveness and feasibility of the proposed multiscale sampling strategy. Additionally, present study revealed that the spatial data close to the landslide location might be more suitable to predict the probability of the landslide occurrence. Finally, we expect that the deep learning method based on multiscale data representation will advance our ability to assess the landslide susceptibility and raise the awareness of landslide disasters.

Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region

Flash floods are responsible for loss of life and considerable property damage in many countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by land-use planners and emergency managers. The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach (DBPGA) based on Deep Belief Network (DBN) with Back Propagation (BP) algorithm optimized by the Genetic Algorithm (GA). For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation (ORAE) technique. Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model. Statistical metrics include sensitivity, specificity accuracy, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC) were used to assess the validity of the proposed model. The result shows that the proposed model has the highest goodness-of-fit (AUC = 0.989) and prediction accuracy (AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR (0.885), LMT (0.934), BLR (0.936), ADT (0.976), NBT (0.974), REPTree (0.811), ANFIS-BAT (0.944), ANFIS-CA (0.921), ANFIS-IWO (0.939), ANFIS-ICA (0.947), and ANFIS-FA (0.917). We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm

Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling

Climate change weakens the positive effect of human activities on karst vegetation productivity restoration in southern China

Inland waters, characterized by small scale but a large number, play an important role in the carbon budget and global carbon cycle. Colored dissolved organic matter (CDOM) is a significant indicat...

Applicability evaluation of Landsat-8 for estimating low concentration colored dissolved organic matter in inland water

Urban scenery lakes are not only popular sites for recreation, water sport, and visit of citizens and tourists, but also function for urban water drainage and storage, and hence, their water quality is a sensitive indicator of urban aquatic environment. This study focused on two important water quality parameters, TSM (total suspended matter) and CDOM (colored dissolved organic matter) in West Lake, a world famous urban scenery lake in Hangzhou, China. Based on Landsat 8 images, remote sensing inversion models for TSM and CDOM in West Lake were compared and the best ones were determined using exponential functions and green/red band ratios. Results show that the accuracy of TSM models is relatively better while CDOM models performed relatively poor, indicating that empirical estimation of CDOM for inland complex waters remains a challenge. The Landsat 8-derived results in West Lake from 2013 to 2017 show that TSM presented diverse distributions in different sections and months/seasons-the relatively high-concentration TSM was usually found in the north section and during spring festivals, holidays and summer vacations. These spatiotemporal patterns demonstrate the evidence that TSM in West Lake was highly linked to anthropogenic impacts, such as tourist visits, commercial activities, and urban drainage projects in Hangzhou. The relations between TSM and precipitation were also examined, but no significant correlations were found, showing that the impacts of rainfall on water quality of urban lakes are complicated, and using remote sensing for such studies is still with limitations. PRACTITIONER POINTS: Water quality (TSM and CDOM) of urban scenery lakes is highly influenced by urban tourist activities. Precipitation and TSM have not found significant relations in urban scenery lakes. An exponential model based on Landsat 8's bands 3 and 4 is good for TSM estimation. CDOM estimation remains a challenge for water color remote sensing in urban scenery lakes.

Shuna Pang

Papers

Applicability evaluation of Landsat-8 for estimating low concentration colored dissolved organic matter in inland water

Landsat 8-observed water quality and its coupled environmental factors for urban scenery lakes: A case study of West Lake

Spatiotemporal variations of total suspended matter in complex archipelagic regions using a sigmoid model and Landsat-8 imagery