Flood Prediction Using Machine Learning Models

Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy

Consumption-Based CO2 Emissions on Sustainable Development Goals of SAARC Region

Consumption-based CO2 emission (CBE) accounting shows the possibility of global carbon leakage. Very little attention has been paid to the amount of emissions related to the consumption of products and services and their impact on sustainable development goals (SDGs), especially in the SAARC region. This study used a CBE accounting method to measure the CO2 emissions of five major SAARC member countries. Additionally, a Fully Modified Ordinary Least Square (FMOLS) and a causality model were used to investigate the long-term effects of the CBE and SDG variables between 1972 and 2015. The results showed that household consumption contributed more than 62.39% of CO2 emissions overall in the SAARC region. India had the highest household emissions, up to 37.27%, and Nepal contributed the lowest, up to 0.61%. The total imported emissions were the greatest in India (16.88 Gt CO2) and Bangladesh (15.90 Gt CO2). At the same time, the results for the long-term relationships between the CBEs and SDGs of the SAARC region showed that only the combustible renewables and waste (CRW) variable is significant for most of these countries. The sharing of the responsibility for emissions between suppliers and customers could encourage governments and policymakers to make global climate policy and sustainable development decisions,which are currently stalled by questions over geographical and past emission inequities

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Ancestral Caenorhabditis elegans Cuticle Suppresses rol-1

Genetic background commonly modifies the effects of mutations. We discovered that worms mutant for the canonical rol-1 gene, identified by Brenner in 1974, do not roll in the genetic background of the wild strain CB4856. Using linkage mapping, association analysis and gene editing, we determined that N2 carries an insertion in the collagen gene col-182 that acts as a recessive enhancer of rol-1 rolling. From population and comparative genomics, we infer the insertion is derived in N2 and related laboratory lines, likely arising during the domestication of Caenorhabditis elegans, and breaking a conserved protein. The ancestral version of col-182 also modifies the phenotypes of four other classical cuticle mutant alleles, and the effects of natural genetic variation on worm shape and locomotion. These results underscore the importance of genetic background and the serendipity of Brenner’s choice of strain

Factors affecting mobile phone usage by the farmers in receiving information on vegetable cultivation in Bangladesh

This paper mainly focuses on identifying the factors affecting the use of mobile phone by the farmers and determining the extent of use of mobile phone by the farmers in receiving information on vegetable cultivation. The study was carried out at three villages under Mymensingh sub- district of Mymensingh district in Bangladesh. Seventy farmers were interviewed using structured questionnaire. Appropriate scales were used in order to measure the concerned variables. Both descriptive and inferential statistics were used to analyze the collected data. The majority (70 %) of the vegetable farmers were low user of mobile phone compared to 30 percent were medium user. None of them was found under high mobile phone user. Vegetable farmers’ characteristics such as education and social participation had significant positive relationships with their use of mobile phone while age and farming experience had significant relationships with negative trend. Among them age alone explained 33.1 per cent of the variations to mobile phone usage was confirmed by the step-wise multiple regression model. However, age and social participation were the influential factors affecting the use of mobile phone by the farmers. Lack of mobile servicing centre, expensiveness and electricity problem were the major constraints that cause hindrance to the use of mobile phone in receiving information on vegetables cultivation. Government should take initiatives to ensure proper electricity supply in village area and provide subsidy to easily purchase of mobile phone by the farmers. Besides, field extension agents should encourage and assist the farmers to use mobile phone in receiving information on vegetable cultivation