Browsing by Author "Nejat, Ali (TTU)"
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Item A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models(2022) Dhakal, Rabin (TTU); Sedai, Ashish (TTU); Pol, Suhas (TTU); Parameswaran, Siva (TTU); Nejat, Ali (TTU); Moussa, Hanna (TTU)The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.Item Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides(2023) Yosofvand, Mohammad (TTU); Khan, Sonia Y. (TTUHSC); Dhakal, Rabin (TTU); Nejat, Ali (TTU); Moustaid-Moussa, Naima (TTU); Rahman, Rakhshanda Layeequr (TTUHSC); Moussa, Hanna (TTU)Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.Item Family structures, relationships, and housing recovery decisions after Hurricane Sandy(2016) Nejat, Ali (TTU); Cong, Zhen (TTU); Liang, Daan (TTU)Understanding of the recovery phase of a disaster cycle is still in its infancy. Recent major disasters such as Hurricane Sandy have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. In this setting, a thorough grasp of housing recovery decisions can lead to effective post-disaster planning by policyholders and public officials. The objective of this research is to integrate vignette and survey design to study how family bonds affected rebuilding/relocating decisions after Hurricane Sandy. Multinomial logistic regression was used to investigate respondents' family structures before Sandy and explore whether their relationships with family members changed after Sandy. The study also explores the effect of the aforementioned relationship and its changes on households' plans to either rebuild/repair their homes or relocate. These results were compared to another multinomial logistic regression which was applied to examine the impact of familial bonds on respondents' suggestions to a vignette family concerning rebuilding and relocating after a hurricane similar to Sandy. Results indicate that respondents who lived with family members before Sandy were less likely to plan for relocating than those who lived alone. A more detailed examination shows that this effect was driven by those who improved their relationships with family members; those who did not improve their family relationships were not significantly different from those who lived alone, when it came to rebuilding/relocation planning. Those who improved their relationships with family members were also less likely to suggest that the vignette family relocate. This study supports the general hypothesis that family bonds reduce the desire to relocate, and provides empirical evidence that family mechanisms are important for the rebuilding/relocating decision-making process.Item Institutional foundations of adaptive planning: exploration of flood planning in the Lower Rio Grande Valley, Texas, USA(2023) Ross, Ashley D.; Nejat, Ali (TTU); Greb, VirgieGiven the risk posed by escalating climate conditions, there is a need to assess how localities integrate adaptive planning into hazard mitigation and how this is enabled or constrained by existing planning institutions. We explore this for flood planning in the Lower Rio Grande Valley of Texas, United States–a largely underresourced and highly socioeconomically vulnerable area. Using Natural Language Processing to analyze county and regional hazard plans as well as transcripts of regional flood planning meetings, we find that adaptive planning is largely absent in the study area. Like many localities in the U.S., the communities in the study area have approached flood planning in static terms that do not fully consider future uncertainties; failed to engage diverse participation in planning; and neglected to pursue co-benefits possible with flood mitigation and other sectors. Critically, this may be a product of traditional planning institutions as well as limited local capacities.Item Recovus: An agent-based model of post-disaster household recovery(2020) Moradi, Saeed; Nejat, Ali (TTU)The housing sector is an important part of every community. It directly affects people, constitutes a major share of the building market, and shapes the community. Meanwhile, the increase of developments in hazard-prone areas along with the intensification of extreme events has amplified the potential for disaster-induced losses. Consequently, housing recovery is of vital importance to the overall restoration of a community. In this relation, recovery models can help with devising data-driven policies that can better identify pre-disaster mitigation needs and post-disaster recovery priorities by predicting the possible outcomes of different plans. Although several recovery models have been proposed, there are still gaps in the understanding of how decisions made by individuals and different entities interact to output the recovery. Additionally, integrating spatial aspects of recovery is a missing key in many models. The current research proposes a spatial model for simulation and prediction of homeowners’ recovery decisions through incorporating recovery drivers that could capture interactions of individual, communal, and organizational decisions. RecovUS is a spatial agent-based model for which all the input data can be obtained from publicly available data sources. The model is presented using the data on the recovery of Staten Island, New York after Hurricane Sandy in 2012. The results confirm that the combination of internal, interactive, and external drivers of recovery affect households’ decisions and shape the progress of recovery.