Some economic benefits of a synchronous earth observatory satellite

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution

Forecasting Harmful Algal Blooms for Western Lake Erie Using Data Driven Machine Learning Techniques

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, artificial neural network (ANN) and classification and regression tree (CART), to forecast monthly HAB indicators in western Lake Erie for July to October. ANN and CART models were created with two methods of selecting input variables and two training periods: 2002 to 2011 and 2002 to 2013. First a nutrient loading period method which considers all nutrient contributing variables averaged from March to June and second a Spearman rank correlation to choose separate input sets for each month considering 224 different combinations of averaging and lag periods. The ANN models showed a correlation coefficient increase from 0.70 to 0.77 for the loading method and 0.79 to 0.83 for the Spearman method when extending the training period. The CART models followed a similar trend increasing overall precision from 85.5% to 92.9% for the loading method and 82.1% to 91% for the Spearman method. Both selection methods had similar variable importance with river discharge and phosphorus mass showing high importance across all methods. The major limitation for ANN is the time required for each forecast to be complete while the CART forecasts earlier is only able to produce a class forecast. In future work, the ANN model accuracy can be improved and use new sets of variables to allow earlier HAB forecasts. The final form of ANN and CART models will be coded in a user interface system to forecast HABs. The monthly forecasting system developed allows watershed planners and decision-makers to timely manage HABs in western Lake Erie

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development

Development and Application of Tools for Avalanche Forecasting, Avalanche Detection, and Snowpack Characterization

Avalanche formation is a complex interaction between the snowpack, weather, and terrain. However, detailed observations typically can only be made at a single point and must be extrapolated over the slope or regional scale. This study aims to provide avalanche forecasters with tools to evaluate the snowpack, avalanche hazard, and avalanche occurrence when manual observations are not feasible. Avalanches that occur within the new storm snow are a prevalent problem for the avalanche forecasters with the Idaho Transportation Department (ITD) along Highway 21. We have implemented a real time SNOw Slope Stability (SNOSS) model that provides an index to the stability of that layer. SNOSS has been run real time starting during the winter of 2011/2012 with model results outputted to a webpage for easy viewing by avalanche forecasters. To further improve the accuracy of SNOSS, the model was evaluated with a large database of avalanches from the Utah Department of Transportation (UDOT). Using weather data and SNOSS results, the probability of an avalanche day producing a natural direct action avalanche was calculated using a Balanced Random Forest (BRF). In the future, we hope that the BRF can provide a probability of an avalanche occurrence given the current weather and snowpack conditions that can be utilized by avalanche forecasters in their normal operations. The concern for avalanche forecasters with highway operations is the threat of an avalanche releasing and hitting a highway. Infrasound generated by an avalanche moving downhill can be detected and tracked using array processing techniques. This will allow avalanche forecasters to evaluate the avalanche hazard more effectively by determining when and where avalanches have occurred. An avalanche detection system has been developed to detect avalanches in near real time using infrasound arrays. The system processes the infrasound data on-site, automatically detects events, and classifies the events using multiple neural networks. If an avalanche has been detected, the system will transmit the necessary information over satellite to be viewed by avalanche forecasters on a webpage

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Proceedings of the XXVIIIth TELEMAC User Conference 18-19 October 2022

Hydrodynamic

Corrosion Behavior of H-Pile Steel in Different Soils

This dissertation aimed to study the corrosion performance of carbon steel in different soils, collected from the state of Wisconsin. Carbon steel specimens (as-received) as well as steel embedded in mortar (steel-mortar) specimens, to simulate the realistic H-pile design in bridges, were used in this investigation. Both as-received steel and steel-mortar specimens were embedded in as-received soils, with different physiochemical properties, i.e. pH, moisture content, resistivity, chloride content, sulfate and sulfite contents, and the mean total organic carbon concentration, for more than one year. Both specimen types were also embedded in the same as-received soils, but with increased chloride content to 3% by weight of chloride ions for more than one year. In addition, the surface of three identical as-received specimens was modified using the sandblasting method for 5 minutes. These specimens were embedded in one of the collected soils. Different electrochemical measurements were conducted on the specimens to evaluate the corrosion activity of the steel in these soils. The results showed a comparable corrosion activity of the steel-mortar specimens in all soils compared to the as-received specimens in the same soil both with and without chlorides, except for soils collected from Wausau. No correlation between the available physiochemical data and the observed results was determined. No information on the type and population of the bacteria in the collected soils was available. Perhaps, this information could explain the observed results. In all cases, there was a galvanic current flowing between specimens in chloride-free and chloride contaminated soils. In addition, corrosion potential values of all specimens remained relatively stable both before and after addition of chlorides, suggesting just measuring the corrosion potential may not be an efficient method to monitor the change of corrosion behavior of steel in the soil. The results of electrochemical experiments also showed significant improvement in corrosion resistance of sandblasted specimens compared to the as-received specimens

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk