Use of Machine Learning in Evaluation of Drought Perception in Irrigated Agriculture: The Case of an Irrigated Perimeter in Brazil

Priority of Water Allocation during Drought Periods: The Case of Jaguaribe Metropolitan Inter-Basin Water Transfer in Semiarid Brazil

Inter-basin water transfers are the root of many conflicts, and water scarcity accentuates them. Those conflicts involve the priority of water use between regions. The Jaguaribe Metropolitan system, located in the Brazilian semiarid region, presents conflicts amongst different water users: irrigated perimeters, industry, and households. This paper analyzed the Jaguaribe Metropolitan water transfer during the 2012–2018 drought by considering environmental and societal aspects. Changes in consumption and users’ drought perception were assessed. The results showed that the drought was longer and more severe in the region that provided water (i.e., Jaguaribe) than in the region that received it (i.e., FMR). Jaguaribe irrigators were aware of the beginning of the drought, but it did not result in immediate consumption control. On the other hand, drought perception was delayed in the FMR. The results of this study suggested that the water allocation decision-making process should include not only the water demands but also the characteristics of the drought and how people perceive it. The main strategy for improving water governance seems to be promoting integrated regional planning and the empowerment of participatory management.

Multitemporal Spatial Analysis of Land Use and Land Cover Changes in the Lower Jaguaribe Hydrographic Sub-Basin, Ceará, Northeast Brazil

Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The aquaculture activity in this area began in the early 1990s and is currently one of the largest shrimp producers in Brazil. This study generated a spatial-temporal analysis of vegetation index and land use and land cover (LULC) using remote sensing images from Landsat satellites processed using geographic information systems (GIS). The findings showed an increase in the water bodies class where shrimp farms are found. In addition, to help us discuss the results, data from the Global Surface Water Explorer was also used to understand this change throughout intra and interannual water variability. Besides shrimp farms’ intensification, agricultural areas in the LJRSB also increased, mainly in the irrigated perimeter lands (IPLs), causing a loss in the Caatinga native vegetation. In summary, over recent years, significant changes have been noticeable in the LJRSB coastal region, caused by an increase in shrimp farms mainly located on the Jaguaribe River margins, destroying the native riparian forest.

Drought Assessment in São Francisco River Basin, Brazil: Characterization through SPI and Associated Anomalous Climate Patterns

The São Francisco River Basin (SFRB) is one of the main watersheds in Brazil, standing out for generating energy and consumption, among other ecosystem services. Hence, it is important to identify hydrological drought events and the anomalous climate patterns associated with dry conditions. The Standard Precipitation Index (SPI) for 12 months was used to identify hydrological drought episodes over SFRB 1979 and 2020. For these episodes, the severity, duration, intensity, and peak were obtained, and SPI-1 was applied for the longest and most severe episode to identify months with wet and dry conditions within the rainy season (Nov–Mar). Anomalous atmospheric and oceanic patterns associated with this episode were also analyzed. The results revealed the longest and most severe hydrological drought episode over the basin occurred between 2012 and 2020. The episode over the Upper portion of the basin lasted 103 months. The results showed a deficit of monthly precipitation up to 250 mm in the southeast and northeast regions of the country during the anomalous dry months identified through SPI-1. The dry conditions observed during the rainy season of this episode were associated with an anomalous high-pressure system acting close to the coast of Southeast Brazil, hindering the formation of precipitating systems.

Machine Learning in Agriculture: A Comprehensive Updated Review

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh

The transmission of novel coronavirus (COVID-19) can be reduced by implementing a lockdown policy, which has also been proven as an effective control measure for air pollution in the urban cities. In this study, we applied ground- and satellite-based data of five criteria air pollutants (PM2.5, NO₂, SO₂, O₃, and CO) and meteorological factors from March 8 to May 15, 2020 (before, partial-, and full-lockdown). The generalized additive models (GAMs), wavelet coherence, and random forest (RF) model were employed to explore the relationship between air quality indicators and COVID-19 transmission in Dhaka city. Results show that overall, 26, 20.4, 17.5, 9.7 and 8.8% declined in PM 2.5, NO₂, SO₂, O₃, and CO concentrations, respectively, in Dhaka City during the partial and full lockdown compared to the period before the lockdown. The implementation of lockdown policy for containing COVID-19 transmission played a crucial role in reducing air pollution. The findings of wavelet coherence and partial wavelet coherence demonstrate no standalone coherence, but interestingly, multiple wavelet coherence indicated a strong short-term coherence among air pollutants and meteorological factors with the COVID-19 outbreak. Outcomes of GAMs indicated that an increase of 1-unit in long-term exposure to O₃ and CO (lag1) was associated with a 2.9% (95% CI: -0.3%, -5.6%), and 53.9% (95% CI: 0.2%, -107.9%) decreased risk of COVID-19 infection rate during the full-lockdown period. Whereas, COVID-19 infection and MT (mean temperature) are modulated by a peak during full-lockdown, which is mostly attributed to contact transmission in Dhaka city. RF model revealed among the parameters being studied, MT, RH (relative humidity), and O₃ were the dominant factors that could be associated with COVID-19 cases during the study period. The outcomes reported here could elucidate the effectiveness of lockdown scenarios for COVID-19 containment and air pollution control in Dhaka city.

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.