Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.

Despite living in a rural country, farmers in India face several challenges. Every year, they suffer significant losses due to agricultural insect infestation. These losses are primarily the result of inadequate field surveillance, crop diseases, and ineffective pesticide management. We need cutting-edge technology that is constantly evolving to maintain control over such major concerns responsible for output reductions year after year. Wireless sensor networks address all of these issues; in fact, wireless sensor network technology is quickly becoming the backbone of modern precision agriculture. We propose a strategy for pest monitoring using wireless sensor networks in this study by simply recognizing insect behaviour using various sensors. We proposed a rapid and accurate insect detection and categorization approach based on five important crops and associated insect pests. This method examines insect behaviour by collecting data from sensors placed in the field. The results show that the proposed work improves the accuracy of the existing work by 3.9 percent.

A leading cause of death from natural disasters over the last 50years is witnessed by none other than earthquake occurrences which have a negative economic impact on the world and claimed thousands of lives over the years, causing devastation to properties. In this paper, a novel Ensemble Earthquake Prediction Method (EEPM) is proposed and implemented to produce a strong learner (ensemble method) having better accuracy in prediction, less variance, and less errors. Data (parameters) which is continuous in nature is collected from two countries, India and Nepal, for five years, and surveyor’s data (precursor) which is categorical in nature is collected from three countries India, Nepal, and Kenya for five years on the specific earthquake-prone regions. The preprocessed data is generated by combining parameters and precursor data. EEPM focuses on detecting the accurate and better early signs of an earthquake and finding the probability of occurrence of an earthquake in the specified region, i.e., better prediction and robustness. The results of EEPM produced better and less variance and less error in comparison to individual machine learning methods as well as better accuracy 87.8%, compared to state-of-the-art ensemble methods. The prediction of earthquake will alarm not only the people of the society but also the different organizations to explain the appropriate range of magnitude and dynamics of occurrence of earthquake.

A Fuzzy Ecosystem Benchmarking for Crowdfunding in Transport Sector: The global crowdfunding (CF) market was valued at 10.2 billion US$ in 2018 and is expected to almost triple in size by 2025. The CF is evolving as a major and easy source of fundraising methods for various industries. Still, this acceptability is not widely accepted in transportation activities due to various limitations and low awareness among policymakers. The present research analyzes the factors contributing to the growth of market acceptability of CF, divided into three different research phases: identifying barriers from the literature, interviews with transport industry experts at two stages, and designing an ISM model in a fuzzy environment. The identification phase led to selecting 16 factors from the past literature and suggesting industrial experts. The Interpretive Structural Modelling (ISM) analysis was used to understand the impact and linkage of identified barriers on seven levels of the fuzzy scale. The factors are classified into four major categories based on the fuzzy matrix's drive and dependence power using Fuzzy MICMAC. The sixteen identified growth factors for CF have been distributed in 5 levels in the ISM designed model. All the factors had fallen in only two quadrants of MICMAC based on the fuzzy scale matrix. Except for No or Low in regulation, the selected fifteen factors fall in the linkage quadrant, with high dependency and driving power. Such relation of all variables is the precise reason for storm growth in the field. No or Low in regulation is one of the most significant factors to the growth and acceptance of this innovative fundraising method by common investors but cannot be controlled directly by the associated crowdfunding members in the transport industry.

The above article from IET Communications, published online on 4 November 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

Technological advancement with the rapid growth of population has brought a high impact on society's livelihood. Due to that, the consumption pattern has increased, and on the other hand, waste generation has been rise unconditionally that ultimately leads to garbage waste problems like overflowing. It gives challenges to waste monitors due to a lack of information about trash bins and its periodic collection of waste. So, to solve those issues, the Internet-of-things can be implemented. The garbage monitoring system, which is implemented using the Internet of things and provides real-time sensor information to authorized personals and dynamically, a dustbin will be empty on time. Garbage Monitoring systems that use the Internet of things are mainly to overcome contemporary systems, reducing energy consumption and maximizing operations by providing real-time information about trash bins to concerned people.