Integrated gene co-expression network analysis and experimental validation revealed potential targets of human urine extract CDA-II in treating chronic myeloid leukemia.

BACKGROUND: Cell differentiation agent II (CDA-II) exhibits potent anti-proliferative and apoptosis-inducing properties against a variety of cancer cells. However, its mechanism of action in chronic myeloid leukemia (CML) remains unclear. METHODS: Cell counting Kit 8 (CCK-8) and flow cytometry were used to investigate the effects of CDA-II on the biological characteristics of K562 cells. Gene (mRNA and lncRNA) expression profiles were analyzed by bioinformatics to screen differentially expressed genes and to perform enrichment analysis. The Pearson correlation coefficients of lncRNAs and mRNAs were calculated using gene expression values, and a lncRNA/mRNA co-expression network was constructed. The MCODE and cytoHubba plugins were used to analyze the co-expression network. RESULTS: The Results, derived from CCK-8 and flow cytometry, indicated that CDA-II exerts dual effects on K562 cells: it inhibits their proliferation and induces apoptosis. From bioinformatics analysis, we identified 316 mRNAs and 32 lncRNAs. These mRNAs were predominantly related to the meiotic cell cycle, DNA methylation, transporter complex and peptidase regulator activity, complement and coagulation cascades, protein digestion and absorption, and cell adhesion molecule signaling pathways. The co-expression network comprised of 163 lncRNA/mRNA interaction pairs. Notably, our analysis results implicated clustered histone gene families and five lncRNAs in the biological effects of CDA-II on K562 cells. CONCLUSION: This study highlights the hub gene and lncRNA/mRNA co-expression network as crucial elements in the context of CDA-II treatment of CML. This insight not only enriches our understanding of CDA-II's mechanism of action but also might provide valuable clues for subsequent experimental studies of CDA-II, and potentially contribute to the discovery of new therapeutic targets for CML.

Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a non-Hodgkin lymphoma with high mortality rates. Small nucleolar RNAs (snoRNAs) are tumor-specific biological markers, but there are few studies on the role of snoRNAs in DLBCL. MATERIALS AND METHODS: Survival-related snoRNAs were selected to construct a specific snoRNA-based signature via computational analyses (Cox regression and independent prognostic analyses) to predict the prognosis of DLBCL patients. To assist in clinical applications, a nomogram was built by combining the risk model and other independent prognostic factors. Pathway analysis, gene ontology analysis, transcription factor enrichment, protein-protein interactions, and single nucleotide variant analysis were used to explore the potential biological mechanisms of co-expressed genes. RESULTS: Twelve prognosis-correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three-snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. DLBCL patients could be divided into high-risk and low-risk cohorts using the risk model, and the high-risk group and activated B cell-like (ABC) type DLBCL were linked with disappointing survival. In addition, SNORD1A co-expressed genes were inseparably linked to the biological functions of the ribosome and mitochondria. Potential transcriptional regulatory networks have also been identified. MYC and RPL10A were the most mutated SNORD1A co-expressed genes in DLBCL. CONCLUSION: Put together, our findings explored the potential biological effects of snoRNAs in DLBCL, and provided a new predictor for DLBCL prediction.

Retracted: Britannin mediates apoptosis and glycolysis of T-cell lymphoblastic lymphoma cells by AMPK-dependent autophagy.

The above article, published online on 19 September 2022 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/abs/10.1002/jbt.23211), has been retracted by agreement between the authors, the journal Editor in Chief, Hari Bhat, and Wiley Periodicals, LLC. The article is being retracted at the authors' request because some of the data underlying this article refer to a different cell line from the one reported in it. As a result, the article's conclusions do not accurately reflect the full data and cannot be considered reliable.

Modelling flood susceptibility based on deep learning coupling with ensemble learning models.

Modelling flood susceptibility is an indirect way to reduce the loss from flood disaster. Now, flood susceptibility modelling based on data driven model is state-of-the-art method such as ensemble learning and deep learning. However, the effect of deep learning coupling with ensemble learning models in flood susceptibility modelling is still unknown. Therefore, the aim of this paper is to propose three deep learning coupling with ensemble learning models by combining the deep learning (DL) with Filtered Classifier (FC), Rotation forest (RF) and Random Subspace (RSS) and explore the effect of coupling method for modelling flood susceptibility. The key step of this paper is as following: firstly, a Dingnan County which is lied in the Jiangxi Province of China is chosen as a case study, single flood event point and random sampling method was applied to generate the flood and non-flood data, respectively, then frequency ratio was utilized to analyze the relationship between each influencing factor and flood occurrence, based on the value of VIF, Spearman's correlation and One R classifier, the result show that there is no multicollinearity between each influencing factor, ten influencing factors have contribution to the flood occurrence and all of them are applied to construct the coupling model. Finally, the DL, FC-DL, RF-DL and RSS-DL were applied to produce flood susceptibility maps. Then, several statistical indexes such as area under the curve (AUC), Kappa index, accuracy (ACC), and F-measure were used to assess the accomplishment of these coupling models. For the train data, the FC-DL model acquired the highest AUC value (0.996), followed by RF-DL (0.944), RSS-DL (0.934), and DL (0.934). For the validation data, the result showed that all models have a good accomplishment (AUC>0.8). In a word, the deep learning coupling with ensemble learning models demonstrates the more reliable and excellent performance. Hence, the proposed new method will help the government for land use planning and can be applied in other area around the world.

Sintilimab combined with chidamide in the treatment of extranodal nature killer/T-cell lymphoma with secondary hemophagocytic lymphohistiocytosis: Two case reports and literature review.

RATIONALE: Extranodal nature killer/T-cell lymphoma (ENKTL) failing in asparaginase-containing treatments is fatal, it has a higher mortality rate when accompanied by secondary hemophagocytic lymphohistiocytosis (HLH). The study reported 2 ENKTL-related HLH patients. PATIENT CONCERNS: Patient 1 visited for nasal congestion and runny nose for 6 months then got a fever and serious myelosuppression after P-GEP (pegaspargase, gemcitabine, etoposide, and methylprednisolone) chemotherapy. Patient 2 complained of painless lymphadenectasis in the right neck for 4 months and experienced recurrent fever and poor performance status after 3 cycles of P-Gemox (pegaspargase, gemcitabine, and oxaliplatin) chemotherapy. DIAGNOSES: Patient 1 and patient 2 were diagnosed as ENKTL failing in asparaginase-based chemotherapy and involving secondary HLH. INTERVENTIONS: The dose of chidamide was 20 mg twice a week for 2 weeks and sintilimab was 200 mg once every 3 weeks. OUTCOMES: ENKTL was relieved and the HLH was resolved after the therapy of sintilimab and chidamide. The patients had achieved durable survival without immune-related adverse events. LESSONS: ENKTL-related HLH needs early diagnosis and treatment. The combined strategy of sintilimab plus chidamide help deal with HLH and solve ENKTL, it may be a useful treatment option for ENKTL-related HLH.

RNA-seq and integrated network analysis reveals the hub genes and key pathway of paclitaxel inhibition on Adriamycin resistant diffuse large B cell lymphoma cells.

About 40% of patients with diffuse large B-cell lymphoma (DLBCL) develop drug resistance after first-line chemotherapy, which remains a major cause of morbidity and mortality. The emergence of DLBCL drug resistance is mainly related to Adriamycin. Our previous research shows that Paclitaxel could be a potential therapeutic drug for the treatment of Adriamycin-resistant DLBCL. Based on the results of RNA-seq and integrated network analysis, we study the potential molecular mechanism of Paclitaxel in the treatment of Adriamycin-resistant DLBCL in multiple dimensions. A CCK-8 assay showed that the inhibitory effect of Paclitaxel on Pfeiffer and Pfeiffer/ADM (Adriamycin-resistant DLBCL cell lines) is significantly higher than that of Adriamycin (P ＜ 0.05). Five hub genes (UBC, TSR1, WDR46, HSP90AA1, and NOP56) were obtained via network analysis from 971 differentially expressed genes (DEGs) based on the RNA-seq of Paclitaxel-intervened Pfeiffer/ADM. The results of the network function module analysis showed that the inhibition of Pfeiffer/ADM by Paclitaxel was closely related to ribosome biosynthesis in eukaryotes. The results of RT-qPCR showed that the mRNA levels of the five hub genes in the Pfeiffer/ADM group were significantly lower than those in the Pfeiffer group and the Pfeiffer/ADM Paclitaxel-treated group (P ＜ 0.05). Consistent with studies, Paclitaxel exhibited a significant inhibitory effect on Adriamycin-resistant DLBCL, which may have played a role in the five hub genes (UBC, TSR1, WDR46, HSP90AA1 and NOP56) and ribosome biosynthesis in eukaryotes pathway, but the specific regulation needs further experimental verification.

Expression of N6-methyladenosine (m6A) regulators correlates with immune microenvironment characteristics and predicts prognosis in diffuse large cell lymphoma (DLBCL).

This study conducted a comprehensive analysis of the clinical significance of N6-methyladenosine (m6A) regulators and their relationship with immune microenvironment characteristics in diffuse large cell lymphoma (DLBCL). Consensus clustering was performed to molecularly discriminate DLBCL subtypesbased on m6A regulators' expression. Using the Cox and Lasso regression algorithm, survival-associated m6A regulators were identified, and a m6A-based prognostic signature was established. The influence of m6A risk on immune cell infiltration, immune checkpoint genes, cancer immunity cycle, and immunotherapeutic response was evaluated. Potential molecular pathways related to m6A risk were investigated using gene set enrichment analysis. The m6A regulators showed satisfactory performance in distinguishing DLBCL subgroups with distinct clinical traits and outcomes. A six m6A regulator-based prognostic signature was established and validated as an independent predictor, which separated patients into low- and high-risk groups. High-risk m6A indicated worse survival. The B cells naïve, T cells gamma delta, and NK cells resting were the three most affected immune cells by m6A risk. Up-regulated (PDCD1 and KIR3DL1) and down-regulated (TIGIT, IDO1, and BTLA) immune checkpoint genes in the high-risk group were identified. The m6A risk was found to influence several steps in the cancer immunity cycle. Patients with high-risk m6A were more likely to benefit from immunotherapy. Biological function enrichment analysis revealed that high-risk m6A to be tended related to malignant tumor characteristics, while low-risk m6A showed trend to be related to defensive response processes. Collectively, the m6A-based prognostic signature could be a practical prognostic predictor for DLBCL and immune microenvironment characteristics affected by m6A may be part of the mechanism.

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

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.

A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.

The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a Repeatedly Random Undersampling (RRU) was proposed to address the randomness problem faced in Single Random Sampling. However, the RRU cannot guarantee that the generated classifier has the best classification performance during the repeatedly random sampling process. To address this weakness, in this study we proposed an optimized RRU, which follows the idea of RRU, and then changing its rule to find a best classifier. Then, the selected classifier, the actual most accurate classifier (MAC), was employed to compute the probability of hazard occurrence. Support Vector Machine (SVM) was selected as the analysis method, and Genetic Algorithm was employed to compute the parameters of SVM. Forest fire susceptibility was assessed in Huichang County in China due to its forest values and frequent fire events. The results indicated that compared with the RRU, the optimized RRU can find out an actual MAC which has the best classification performance among possible MACs; also, the fire susceptibility map generated by the actual MAC comforts to objective facts. The generated fire susceptibility map can provide useful decision supports for local government to reduce forest fire risks. Moreover, the proposed sampling method, the optimized RRU, presented an enhanced approach for selecting negative samples, which makes the results of forest fire susceptibility assessment more reliable and accurate.

Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping.

The main objective of the current study was to present a methodological approach that combines Information Theory, a neural network and meta-heuristic techniques so as to generate a landslide susceptibility map. Specifically, the methodology involved three important tasks: Classifying the landslide related variables, weighting them and optimizing the structural parameters of the neural network. Shannon's entropy index was used to estimate for each landslide related variable the number of classes which maximized the information coefficient, whereas the Certainty Factor method was used to weight the variables. A Neural Network, a (NN) which uses stochastic gradient descent (SGD), the structural parameters of which are optimized by a Genetic Algorithm (GA), was implemented to generate the landslide susceptibility map. A well defined spatial database which included 380 landslides and fourteen related variables (elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index, stream power index, stream transport index, land use cover, distance to road, distance to faults, distance to river, lithology and soil cover) were considered for implementing the NN-SGD-GA model, in the Yanshan County located in Shangrao Municipality, in the north-eastern of Jiangxi province, China. To validate the predictive power of the novel model, a Logistic Regression (LR) and Random Forest (RF) model were used for comparison. The results showed that the NN-SGD-GA model achieved the highest prediction accuracy (88.10%), followed by the RF (86.26%) and the LR (85.82%) models. Furthermore, by analyzing the validation data, concerning the spatial distribution of landslides and the susceptibility index, the proposed model showed an area under curve value of 0.8212, followed by the RF (0.8124) and the LR (0.8020) models. Finally, the proposed model showed the highest relative landslide density value of 65.09, followed by the RF (62.51) and the LR (61.76) models, when using the validation dataset. The novelty of our approach is the usage of an intelligent way to select and classify the most appropriate prognostic variables and also the implementation of an evolutionary wrapper automatic procedure that efficiently generates prediction models with reduced complexity and adequate generalization capacity. Overall, the proposed model can be successfully used for landslide susceptibility mapping as an alternative spatial investigation tool.

Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.

The major target of this study is to design two novel hybrid integration artificial intelligent models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide susceptibility in the Youfanggou district (China). First of all, we prepared a geospatial database in the study area, including 79 landslide points that were divided into a training and validating dataset and 14 landslide conditioning factors. Second, the Support Vector Machines classifier (SVMC) approach was adapted to analyze the predictive capability of the landslide predisposing factors in each method. Then, a multicollinearity analysis using TOL and VIF parameters and Pearson's correlation coefficient methods were applied to verify the multicollinearity and correlation between these factors. Third, the LADT-Bagging and FPA-Bagging models were built by the integration of the LogitBoost alternating decision trees (LADT) with the Bagging ensemble and Forest by Penalizing Attributes (FPA) with the Bagging ensemble, respectively. Besides, heuristic tests were also applied to identify the appropriate values of each model's parameters in order to obtain the best programmer. Finally, for the training dataset, the results reveal that the LADT-Bagging model acquire the largest AUC value (0.980), smallest standard error (SE) (0.0134), narrowest 95% confidence interval (CI) (0.920-0.999), highest accuracy value (AV) (91.03%), highest specificity (94.44%), highest sensitivity (88.10%), highest F-measure (0.9115), lowest MAE (0.2016), lowest RMSE (0.2653), and highest Kappa (0.8205). About the result of validating dataset, it reveal that the LADT-Bagging model acquire the largest AUC value (0.781), the smallest SE (0.0539), the narrowest 95% CI (0.673-0.867), highest AV (71.19%), highest specificity (74.29%), highest sensitivity (69.77%), highest F-measure (0.7195), lowest MAE (0.3509), lowest RMSE (0.4335), and highest Kappa (0.4359). The results indicate that the LADT-Bagging model outperforms the FPA-Bagging, LADT and FPA models. Furthermore, the results of a Wilcoxon signed-rank test demonstrate that LADT-Bagging is significantly statistically different from other models. Therefore, in this study, the proposed new models are useful tools for land use planners or governments in high landslide risk areas.

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models.

The present study is carried out in the context of the continuous increase, worldwide, of the number of flash-floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by flash-flood phenomena. Therefore, Flash-Flood Potential Index (FFPI) was defined and calculated across the Bâsca Chiojdului river basin by using one bivariate statistical method (Statistical Index) and its novel ensemble with the following machine learning models: Logistic Regression, Classification and Regression Trees, Multilayer Perceptron, Random Forest and Support Vector Machine and Decision Tree CART. In a first stage, the areas with torrentiality were digitized based on orthophotomaps and field observations. These regions, together with an equal number of non-torrential pixels, were further divided into training surfaces (70%) and validating surfaces (30%). The next step of the analysis consisted of the selection of flash-flood conditioning factors based on the multicollinearity investigation and predictive ability estimation through Information Gain method. Eight factors, from a total of ten flash-floods predictors, were selected in order to be included in the FFPI calculation process. By applying the models represented by Statistical Index and its ensemble with the machine learning algorithms, the weight of each conditioning factor and of each factor class/category in the FFPI equations was established. Once the weight values were derived, the FFPI values across the Bâsca Chiojdului river basin were calculated by overlaying the flash-flood predictors in GIS environment. According to the results obtained, the central part of Bâsca Chiojdului river basin has the highest susceptibility to flash-flood phenomena. Thus, around 30% of the study site has high and very high values of FFPI. The results validation was carried out by applying the Prediction Rate and Success Rate. The methods revealed the fact that the Multilayer Perceptron - Statistical Index (MLP-SI) ensemble has the highest efficiency among the 3 methods.

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages.

Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China.

Assessments of landslide disasters are becoming increasingly urgent. The aim of this study is to investigate a convolutional neural network (CNN) framework for landslide susceptibility mapping (LSM) in Yanshan County, China. The two primary contributions of this study are summarized as follows. First, to the best of our knowledge, this report describes the first time that the CNN framework is used for LSM. Second, different data representation algorithms are developed to construct three novel CNN architectures. In this work, sixteen influencing factors associated with landslide occurrence were considered and historical landslide locations were randomly divided into training (70% of the total) and validation (30%) sets. Validation of these CNNs was performed using different commonly used measures in comparison to several of the most popular machine learning and deep learning methods. The experimental results demonstrated that the proportions of highly susceptible zones in all of the CNN landslide susceptibility maps are highly similar and lower than 30%, which indicates that these CNNs are more practical for landslide prevention and management than conventional methods. Furthermore, the proposed CNN framework achieved higher or comparable prediction accuracy. Specifically, the proposed CNNs were 3.94%-7.45% and 0.079-0.151 higher than those of the optimized support vector machine (SVM) in terms of overall accuracy (OA) and Matthews correlation coefficient (MCC), respectively.

Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India.

This research paper analyzed urban spatial pattern and trend of urban growth in Kolkata urban agglomeration, India using urban sprawl matrix during 1990-2000 & 2000-2015. Seven urban classes viz. urban primary core, urban secondary core, sub urban fringe, scatter settlement, urban open space, non-urban area and water body were chosen for analyzing the magnitude and direction of urban expansion. Landsat TM and Landsat 8 OLI satellite data for 1990, 2000 and 2015 were used for assessing land use land cover change, urban land transformation, urban spatial pattern and trend in urban growth. The study revealed that the built up area has increased drastically. This increase in built up area is attributed to decrease in prime agricultural land and open space. The land use/land cover change matrix showed that built up area has expanded by 16.6% during 1990-2000 and 24.5% during 2000-2015. The urban expansion is a result of large share of land transformation from agricultural land at the rate of 153.1% during 1990-2000 and 66.9% during 2000-2015. Analysis of trend of urban growth in 38 municipalities and 3 municipal corporations of Kolkata urban agglomeration revealed that municipalities located along the east bank of river Hooghly and surrounded by Kolkata Municipal Corporation have experienced a very fast urban growth. Urban primary and secondary cores have increased in newly developed municipalities. Sub urban fringe has increased in the municipalities located away from river Hooghly while open space has decreased in all the old municipalities. Pattern of land transformation and trend of urban growth of Kolkata urban agglomeration for the last 25years may help in guiding future planning and policy-making for the urban agglomeration. Integrated approach of remote sensing, GIS and urban sprawl matrix has proved instrumental in analyzing urban expansion and identifying priority areas for effectives planning and management.

Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.

The preparation of a landslide susceptibility map is considered to be the first step for landslide hazard mitigation and risk assessment. However, these maps are accepted as end products that can be used for land use planning. The main goal of this study is to assess and compare four advanced machine learning techniques, namely the Bayes' net (BN), radical basis function (RBF) classifier, logistic model tree (LMT), and random forest (RF) models, for landslide susceptibility modelling in Chongren County, China. A total of 222 landslide locations were identified in the study area using historical reports, interpretation of aerial photographs, and extensive field surveys. The landslide inventory data was randomly split into two groups with a ratio of 70/30 for training and validation purposes. Fifteen landslide conditioning factors were prepared for landslide susceptibility modelling. The spatial correlation between landslides and conditioning factors was analyzed using the information gain (IG) method. The BN, RBF classifier, LMT, and RF models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) and statistical measures, including sensitivity, specificity, and accuracy, were employed to validate and compare the predictive capabilities of the models. Out of the tested models, the RF model had the highest sensitivity, specificity, and accuracy values of 0.787, 0.716, and 0.752, respectively, for the training dataset. Overall, the RF model produced an optimized balance for the training and validation datasets in terms of AUC values and statistical measures. The results of this study also demonstrate the benefit of selecting optimal machine learning techniques with proper conditioning selection methods for landslide susceptibility modelling.

Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China.

The main objective of the present study was to utilize Genetic Algorithms (GA) in order to obtain the optimal combination of forest fire related variables and apply data mining methods for constructing a forest fire susceptibility map. In the proposed approach, a Random Forest (RF) and a Support Vector Machine (SVM) was used to produce a forest fire susceptibility map for the Dayu County which is located in southwest of Jiangxi Province, China. For this purpose, historic forest fires and thirteen forest fire related variables were analyzed, namely: elevation, slope angle, aspect, curvature, land use, soil cover, heat load index, normalized difference vegetation index, mean annual temperature, mean annual wind speed, mean annual rainfall, distance to river network and distance to road network. The Natural Break and the Certainty Factor method were used to classify and weight the thirteen variables, while a multicollinearity analysis was performed to determine the correlation among the variables and decide about their usability. The optimal set of variables, determined by the GA limited the number of variables into eight excluding from the analysis, aspect, land use, heat load index, distance to river network and mean annual rainfall. The performance of the forest fire models was evaluated by using the area under the Receiver Operating Characteristic curve (ROC-AUC) based on the validation dataset. Overall, the RF models gave higher AUC values. Also the results showed that the proposed optimized models outperform the original models. Specifically, the optimized RF model gave the best results (0.8495), followed by the original RF (0.8169), while the optimized SVM gave lower values (0.7456) than the RF, however higher than the original SVM (0.7148) model. The study highlights the significance of feature selection techniques in forest fire susceptibility, whereas data mining methods could be considered as a valid approach for forest fire susceptibility modeling.

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofE-RF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies.

Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.

Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithologic parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for all models was >0.80. The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the RMSE and MSE methods, the ANFIS-DE hybrid model is more suitable for flood susceptibility mapping in the study area. The proposed method is adaptable and can easily be applied in other sites for flood management and prevention.