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Manish Pandey

Bio: Manish Pandey is an academic researcher from Chandigarh University. The author has contributed to research in topics: Drainage basin & Flood myth. The author has an hindex of 6, co-authored 19 publications receiving 110 citations. Previous affiliations of Manish Pandey include Banaras Hindu University & Jawaharlal Nehru University.

Papers
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Journal ArticleDOI
TL;DR: Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models.

108 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin, and found that 12 major flood explanatory factors were included.
Abstract: This work focuses on comparing results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin. Following inclusivity rule, 12 major flood explanatory factors incl...

84 citations

Journal ArticleDOI
04 Jan 2021-Sensors
TL;DR: In this paper, the authors assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models.
Abstract: There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

35 citations

Journal ArticleDOI
TL;DR: This work showed an ordered and multi-level spatial evaluation of the models’ performance into the decision-making process of selecting the optimum approach among them, and indicated that the MLP_Markov performed excellently, followed by CA_ Markov and ST_MarkOV simulation models.

34 citations

Journal ArticleDOI
TL;DR: The Izvorul Dorului river basin from Romania is investigated, to identify slop, which contributes to flash floods in various regions of the world, causing serious damage to life and property.
Abstract: Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slop...

20 citations


Cited by
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Proceedings Article
01 May 2009
TL;DR: The integrative approach helps in prioritizing and formulating the funding requests to combine airspace, environmental, and procedures development and delivers optimum benefits for the air traffic and carrier community.
Abstract: The Federal Aviation Administration (FAA), with its NextGen Air Transportation System (NextGen) and Performance-Based Navigation (PBN) initiatives, is moving towards a concept of integrated procedures implementation. Performance-Based Navigation initiatives include implementing Area Navigation (RNAV) and Required Navigation Performance (RNP) routes and procedures. The integrative concept of implementation of these procedures would mean a migration away from site by site (or runway by runway) procedure implementation process towards a NextGen readiness concept. This concept will include development of an integrated system of PBN routes and procedures by geographic area (incorporating metro areas and outlying airports). This concept delivers optimum benefits for the air traffic and carrier community. In addition, the integrative approach helps in prioritizing and formulating the funding requests to combine airspace, environmental, and procedures development. This paper discusses different aspects of this integrative approach.

490 citations

01 Jan 2003
TL;DR: In this article, students were asked to answer the following questions: • What is organic agriculture? • Where do I go to get certification in organic agriculture; • How can I get more information about organic agriculture and what is organic?
Abstract: At the end of the class, students will be able to answer the following questions: • What is organic agriculture? • Where do I go to get certification in organic agriculture? • How can I get more information about organic agriculture? What is organic? • The term " organic " is not synonymous to the terms " natural " or " eco-friendly. " • The label " natural " on foodstuff does not guarantee complete adherence to organic practices as defined by a law. • Produce food of high quality in sufficient quantity • Maintain biological diversity within the farming system • Maintain long-term soil fertility • Rely on renewable resources in locally organized agricultural systems • Minimize pollution and protect the environment What is allowed in organic crop production? • New varieties of crops and agricultural technologies • Crop rotations, cover crops and natural-based products that maintain soil fertility • Biological, cultural and physical methods to limit pest expansion and increase population of beneficial insects

367 citations

Journal ArticleDOI
TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
Abstract: Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

195 citations

Journal ArticleDOI
TL;DR: Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models.

108 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results.
Abstract: The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal–Wallis test and Kolmogorov–Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages.

105 citations