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Landslide susceptibility mapping (LSM) founded on DEM is a growing research field with profound implications for human safety and infrastructure preservation. Many existing methods rely on extensive input data to enhance predictive accuracy. This paper aims to introduce a remote sensing-data-requirement framework for LSM. Our approach exclusively leverages a single ALOS PALSAR image, comprising three key steps: (1) Pre-processing, (2) derivation of explanatory variables, and (3) neural network modeling. To begin, we extracted 22 input variables from the ALOS PALSAR image. These variables played a pivotal role in developing the Neural Network (NN) predictor. The predictor structure consists of 22 variables in the input layer, 150 neurons in the hidden layer, and a single output layer. Our model was trained using 5,829 sample points, and subsequently, it was employed to generate landslide susceptibility (LS) map with 745,810 points. Based on the Overall accuracy metric, the model exhibited impressive performance accuracy, achieving 89.3% training and 82.3% testing accuracies. Additionally, it demonstrated a strong performance of 95.22% during training and 84.7% during testing according to the ROC curve. In conclusion, the implementation of our proposed method underscores its ability to develop remarkable accuracy model with remote sensing-data-requirement. This framework offers valuable insights for future progress in regions with challenging conditions and extensive data coverage. Moreover, it effectively handles data quality inconsistencies and data updating issues. [ABSTRACT FROM AUTHOR]

Flooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e. Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development. [ABSTRACT FROM AUTHOR]

This study evaluates the concentration of PM10, PM2.5, NOx, NO2, CO and SO2 parameters and the four climatological parameters (temperature, wind speed, humidity and net radiation flux) during the four seasons. Various statistical techniques were utilized to study the behavior of the selected parameters during the seasons. Descriptive statistics exhibited that the studied parameters have high concentrations in winter, except for NO2(which has a high concentration in autumn), while the concentrations of those parameters were the lowest in summer, except for NO2 and NOX (which have high concentrations in spring). Factor analysis (FA) showed that more than 80% of the total variation belongs to two factors, where 19.47% of the variation was due to wind speed and humidity, while other parameters were responsible for 62.90% of the total variation. Cluster analysis (CA) evaluated the similarity and dissimilarity between various elements through identifying four clusters representing the seasons; cluster 1: autumn, cluster 2: winter, cluster 3: spring and cluster 4: summer. This clustering indicates that the four seasons are entirely different. The highest dissimilarity was reported between summer and the other seasons. CA also classified all parameters into five statistically different clusters; cluster 1: PM10, PM2.5 and CO; cluster 2: SO2, NOX and NO2; cluster 3: humidity; cluster 4: temperature and radiation and cluster 5: wind speed. This study illustrates the benefits of using multivariate techniques for the evaluation and interpretation of the total variation to get a better picture of the pollution sources/factors and understand the behaviors of the parameters in the air. [ABSTRACT FROM AUTHOR]

Since March 5, 2020, the West Bank has faced a real crisis due to the Coronavirus disease 2019 (COVID-19) pandemic. It has infected 581,678 people and caused 5,382 deaths so far, which has resulted in negative impacts on public health and other aspects of daily life. Based on the data provided by the Palestinian Ministry of Health, we inferred the spatial distribution patterns of the pandemic condition in different communities using Geographic Information System (GIS) analysis for pattern and clustering by studying the impact of urban factors on the number of confirmed COVID-19 cases. Ten urban factors were selected (i.e., population, population density, aging ratio, the hierarchy of services, health services, land use, commercial services, road density, green areas, and open spaces) to check their relation to pandemic severity using a linear model, where five factors showed a globally-significant relation. Then, the Geographically Weighted Regression' model (GWR) was adopted to define their unevenly distributed effects in the urban areas on the northwest bank. Among the five factors, the population factor has the most significant impact on the epidemic situation with a positive correlation. However, a negative correlation has been stated between the area of commercial services per person, population density, hierarchy of services, and health services. Finally, we provide recommendations that coordinate various urban factors to mitigate the pandemic spread. This paper will help decision-makers plan and develop different areas in Palestine and worldwide by better understanding the transmission, occurrence, and diffusion of the COVID-19 pandemic in urban areas. [ABSTRACT FROM AUTHOR]

Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP) neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites. [ABSTRACT FROM AUTHOR]

This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS. [ABSTRACT FROM AUTHOR]