As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

/pdf/comprehensive-overview-of-flood-modeling-approaches-a-review-biw6ievg.pdf

Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

/pdf/above-ground-biomass-estimation-in-oats-using-uav-remote-1sr78unq.pdf

Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

Unmanned aerial vehicles (UAVs) are very promising and convenient flying devices that can be used to execute various missions in both civil and military areas. Especially, they have incomparable advantages to execute tracking, transportation, and even fighting missions in remote areas or severe environments. For UAV applications, Lithium batteries are the most widely used power supply devices. However, the low energy/power density of Lithium batteries would greatly limit the flight endurance or load capacity of UAVs, respectively. Thus, hybrid power systems including Lithium batteries and other energy sources are recommended for high-performance UAVs. This review presents a comprehensive investigation of Lithium batteries for electric and hybrid-electric UAV applications. The investigation includes Lithium battery technologies and development trends in UAV applications, issues of UAVs powered by pure Lithium batteries, hybrid power systems combining Lithium batteries with other energy sources for solving the issues of the pure Lithium battery power system, topology design of Lithium battery-based hybrid power systems for UAVs, and performance optimization of the Lithium battery-based hybrid power systems in various UAV applications. At last, the recommendations for future research priorities are given, which would be very significant for the research and development of high-performance Lithium battery-based power systems for UAVs. 

Comprehensive Investigation on Lithium Batteries for Electric and Hybrid-Electric Unmanned Aerial Vehicle Applications

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

/pdf/uav-multispectral-image-based-urban-river-water-quality-2g40zaxt.pdf

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms - A Case Study of the Zhanghe River, China

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

Impacts of an extreme flood on large wood recruitment and transport processes

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Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives

Impacts of an extreme flood on large wood recruitment and transport processes