Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world’s economy. The variability in climate change, rapid urbanization and fast-growing socio-economic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment. Keywords: Machine Learning, Pluvial Flood, Risk, Fuzzy Rule-Based, Prediction DOI: 10.7176/CEIS/12-1-07 Publication date: January 31st 202

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

CRediT authorship contribution statement: Dr. Aman Arora and Dr. Alireza Arabameri have conceptualized the study, prepared the dataset, and optimized the models. Dr. Manish Pandey has helped in writing the manuscript. Prof. Masood A. Siddiqui, Prof. U.K. Shukla, Prof. Dieu Tien Bui, Dr. Varun Narayan Mishra, and Dr. Anshuman Bhardwaj have helped in improving the manuscript at different stages of this work.Peer reviewedPostprin

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply

A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management

Soil erosion is a severe threat to food production systems globally. Food production in farming systems decreases with increasing soil erosion hazards. This review article focuses on geo-informatics applications for identifying, assessing and predicting erosion hazards for sustainable farming system development. Several researchers have used a variety of quantitative and qualitative methods with erosion models, integrating geo-informatics techniques for spatial interpretations to address soil erosion and land degradation issues. The review identified different geo-informatics methods of erosion hazard assessment and highlighted some research gaps that can provide a basis to develop appropriate novel methodologies for future studies. It was found that rainfall variation and land-use changes significantly contribute to soil erosion hazards. There is a need for more research on the spatial and temporal pattern of water erosion with rainfall variation, innovative techniques and strategies for landscape evaluation to improve the environmental conditions in a sustainable manner. Examining water erosion and predicting erosion hazards for future climate scenarios could also be approached with emerging algorithms in geo-informatics and spatiotemporal analysis at higher spatial resolutions. Further, geo-informatics can be applied with real-time data for continuous monitoring and evaluation of erosion hazards to risk reduction and prevent the damages in farming systems.</jats:p

Assessing Uncertainty Associated with Groundwater and Watershed Problems Using Fuzzy Mathematics and Generalized Regression Neural Networks

When trying to represent an environmental process using mathematical models, uncertainty is an integral part of numerical representation. Physically-based parameters are required by such models in order to forecast or make predictions. Typically, when the uncertainty inherent in models is addressed, only aleatory uncertainty (irreducible uncertainty) is considered. This type of uncertainty is amenable to analysis using probability theory. However, uncertainty due to lack of knowledge about the system, or epistemic uncertainty, should also be considered. Fuzzy set theory and fuzzy measure theory are tools that can be used to better assess epistemic, as well as aleatory, uncertainty in the mathematical representation of the environment. In this work, four applications of fuzzy mathematics and generalized regression neural networks (GRNN) are presented. In the first, Dempster-Shafer theory (DST) is used to account for uncertainty that surrounds permeability measurements and is typically lost in data analysis. The theory is used to combine multiple sources of subjective information from two expert hydrologists and is applied to three different data collection techniques: drill-stem, core, and pump-test analysis. In the second, a modification is made to the fuzzy least-squares regression model and is used to account for uncertainty involved in using the Cooper-Jacob method to determine transmissivity and the storage coefficient. A third application, involves the development of a GRNN to allow for the use of fuzzy numbers. A small example using stream geomorphic condition assessments conducted in the state of Vermont is provided. Ultimately, this fuzzy GRNN will be used to better understand the relationship between the geomorphic and habitat conditions of stream reaches and their corresponding biological health. Finally, an application of the GRNN algorithm to explore links between physical stream geomorphic and habitat conditions and biological health of stream reaches is provided. The GRNN proves useful; however, physical and biological data collected concurrently is needed to enhance accuracy

Sustaining critical transport infrastructure space in megacities: multimodal assessment of railway and road systems in Kano & Lagos — Nigeria

Globalisation has the most tremendous negative effects on the changing landscapes of many cities because of the roles of cities as the de facto economy and haven of liveable socioeconomic advantages. As the urban population grows, particularly in developing countries' mega-cities where transport development faces the most complex challenges, a more sophisticated framework of assessment of critical transportation infrastructure and transportation planning is required. This research aims to investigate transport effects of the complex web of interactions of urban chain processes to bring about a more sustainable (and resilient) transport infrastructure development of mega-cities. The interdisciplinary research concepts which incorporate the development of scenario-based applications and prediction techniques involving qualitative and quantitative frameworks were applied to the two Nigerians most populous cities (Lagos and Kano). The framework includes the analysis of spatial-temporal relationship of transport space and urban land use change, congestion and accessibility, sustainability paradigm and themes and ordering of priorities of the intervention policies based on transportation demand management objectives. Data sources include Landsat images, traffic and demographic data, transportation infrastructure inventories, and collaborative engagement with stakeholders and policymakers via questionnaires, interviews, and checklists. First, spatial-temporal analysis was carried out using remote sensing GIS software for land use classification and CA-Markov model implemented in IDRISI SELVA for temporal prediction and its suitability quality. Next is the assessment of accessibility and congestion pattern of the two cities using a surrogate multi-layer feed-forward and back-propagation model involving input-output and curve fitting (NFTOOL) implemented in artificial neural network wizard of MATLAB. Also, the sustainable paradigm and themes were carried using questionnaire and interview instruments and analysed respectively using SPSS and NVivo softwares. Finally, the priorities of intervention policy decision and quality of infrastructure and services were analysed using hybrid SERVQUAL-AHP models. The spatial-temporal analysis of the two cities produced patterns of rising trends for transport and built-up areas while the other land use classes are receding. For example, Kano transport space had grown from 137km$^2$ in 1984 to 290km$^2$ in 2019 while that Lagos grew from 337km$^2$ to 535km$^2$ in the same period. The dynamics model predicts spatial land requirement of Kano city for transport to reach 410km$^2$ in 2050 while Lagos will be needing 692km$^2$ in the same period. Future prediction of the two cities will be highly unsustainable for transport infrastructure. The congestion profile results put the two cities within congestion indices ranging from 7.5 to 10 on a maximum scale of 10, indicating extreme traffic congestion regimes and inaccessibility in the two cities. The sustainability paradigm comprising literacy, sustainable choices and indicators of sustainable transport are below average exposing poor development in the area. Also, the thematic analysis revealed the preponderance of more negative sentiments from the interview over statements of optimism and progress and it corroborates the findings of sustainability paradigm. Finally, satisfaction quality assessment produced low quality scores of 48% and 49% for Kano and Lagos cities respectively. AHP equally allocated more weight to tangibility which defines infrastructure and service qualities. These values are suggestive of the necessity to infrastructure, public transit systems and management of transport demand in the decision policy making. To deal with rising urbanization trends in Nigerian cities and maintain liveable and accessible urban environments, aggressive push—and—pull policies that improve and increase transport infrastructure quality and drive sustainable transport, promote modal split, reduced motorization, and access control is recommended