9 Algorithms That Changed The Future Pdf

[Peppin Kishore]

Everyday, we use our computers to perform remarkable feats. A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease?

This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more.

John MacCormick is a leading researcher and teacher of computer science. He has a PhD in computer vision from the University of Oxford, has worked in the research labs of Hewlett-Packard and Microsoft, and is currently a professor of computer science at Dickinson College.

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Groundwater potential zone modeling is critical for sustainable water resource management since it helps to allocate this key resource more efficiently for agriculture, industry, and home usage. It permits the identification of locations appropriate for groundwater extraction while preserving the resource, guaranteeing long-term growth and resilience in the face of droughts or climate change6,11,12. Traditional approaches for locating areas with high groundwater potential zone modeling rely heavily on expensive and time-consuming ground surveys6. GIS and Remote sensing is a very useful tool for modeling groundwater water potential zones. Researchers have employed various geospatial techniques for modeling groundwater potential zones such as : the AHP method4,6, the fuzzy logic13, combination of GIS and fuzzy logic14, hybrid multi-criteria approach in Google Earth Engine15, AHP and Fuzzy logic based technique16 etc. Nowadays, the use of machine learning algorithms has increased a lot in the field of groundwater potential zone modeling. Machine learning (ML) algorithms outperform traditional approaches in groundwater potential zone modelling because of their ability to manage complex, non-linear interactions across varied datasets and adapt to changing hydrogeological circumstances. Unlike previous techniques, ML algorithms easily deal with non-linearity, making them crucial for mapping groundwater potential zones and improving decision-making in sustainable water resource management17,18,19. The most appropriate ML algorithm is determined by dataset features, and comparison studies are critical for identifying the method that works best in a certain environment. Various machine learning algorithms such as the: artificial neural network (ANN) algorithm20,21; function model22; the decision tree23; the greatest entropy and random forest (RF) models24; and shannon entropy (SE) to Geographic Information Systems (GIS)25, decision tree18, random forest (RF)26, deep learning27, support vector machine learning model (SVM)17 etc. have been used over the years to detect the groundwater potential zones. ML techniques such as RF, and ANN excel in automating the modelling process, capturing complicated patterns, and making accurate predictions19,26,27. They provide a data-driven strategy that identifies feature relevance for a better understanding of groundwater impacting elements. Due to its accuracy and low cost perspectives the use of ML alongside geospatial tools have become popular over the years in many developing countries especially in the South Asian countries. Ensemble Modelling Framework for groundwater level 2 prediction in Bihar19, groundwater Arsenic and health risk prediction model using ML in Pakistan28, mapping of groundwater productivity potential with ML algorithms in the provincial capital of Baluchistan, Pakistan29, water quality analysis with the help of ML algorithms in Sri Lanka by30 shows the growing interest of ML algorithms in these countries to detect the groundwater modeling. Prediction of groundwater level changes has been done in several circumstances31,32,. Furthermore, researchers predicted how various climate change scenarios might affect groundwater levels33.

Bangladesh is one of the most over populated34,35,36, polluted37,38,39 and disaster-prone countries in the world40,41,42,43. Due to its riverine geography and tropical climate, Bangladesh is fortunate to have access to a considerably greater variety of water sources. Despite issues with arsenic, iron, manganese, and microbial pollution, groundwater is believed to be safer to drink than surface water44,45,46. Groundwater is a major source of drinking water and irrigation for the people of Bangladesh. Groundwater is becoming a significant issue in Bangladesh. Day by day, groundwater is polluted by pathogens and agrochemicals. Furthermore, the groundwater in coastal locations is unsuitable for irrigation and drinking due to rising sea levels and saline soils. The nation experiences a severe water shortage throughout the year, particularly during the dry season, as a result of excessive groundwater extraction, unmanaged surface water contamination, the effects of disasters brought on by climate change, saline intrusion, etc.6,47,48,49. Additionally, the amount of agricultural land is constantly declining, and the increase in impermeable surfaces caused by vegetation has only made matters worse50,51,52. A sizeable population is hence regularly exposed to the risks caused by water shortages and poor water quality. Therefore, it is essential to recognize potential groundwater sources and manage them effectively6,53. Groundwater mapping and zoning are important for sustainable water resource management. Mapping and zoning are essential in Bangladesh, where groundwater is vital to agriculture, drinking water, and industry. The approach helps discover high-potential groundwater sites for efficient and sustainable agriculture while limiting overexploitation in sensitive locations. Mapping helps manage water supplies in highly populated regions to fulfil household needs54. Mapping and zoning are also necessary for informed decision-making due to climatic vulnerabilities, salt intrusion problems, and groundwater quality difficulties. Authorities may promote sustainable water usage, resilient urban development, and groundwater protection by identifying high-potential areas. Groundwater mapping and zoning strengthen communities, ecosystems, and enterprises that use groundwater11.

Fourteen parameters were used in the study (i.e., Curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), Topographic Wetness Index (TWI)). The US Geological Survey provided topographic and land use/land cover data, the Bangladesh Meteorological Department provided weather data, and the Geology Survey of Bangladesh provided geology and soil data, the sample points of field survey were taken from Bangladesh Water Development Board.

Several scientists have mapped groundwater potential by cataloging the locations of springs, wells, and quant. In this analysis, groundwater potential was accounted for as well locations. 200 points representing wells were gathered from different sources and a thorough site survey to create the inventory graph for the research area. As a first stage, we collect information that is not groundwater but is similar to the information utilized in the potential groundwater model. The decision was based on the field survey and the same amount of weight was given to the non-groundwater data (200). All data, groundwater and otherwise, has been arbitrarily split into an 80:20 calibrating and test dataset split. Models are calibrated and validated using both groundwater and non-groundwater training data and testing data, respectively. The data set were divided into two parts with the binary number 0 and 1. Binary number 0 were used as non-groundwater data and 1 was used as groundwater data. Total 160 sample points were used as the input data for training on the contrary, 40 sample points were used the input for the testing the dataset.

Comparing the probability of an event happening to a group of reasonable predictions is one of the most common applications of the likelihood ratio (LR) statistical method67. In the situation of groundwater potentiality prediction, where the occurrence has been classed as groundwater or non-groundwater, LR attempts to discover the most effective method for investigating the link between a certain group of conditioning variables and the presence or absence of groundwater68,69. Furthermore, LR has been considered to have excellent predictive performance in classification applications since it maximizes the likelihood function utilizing its convergence criteria67,70. In the LR model, the conditioning variables refer to the traits or attributes that are utilized for making predictions. The model takes into account these input factors while calculating the likelihood of a specific class. In normal logistic regression, relevant characteristics are chosen based on their impact on predicted accuracy and statistical significance71. The process of conditioning variable selection may employ approaches such as feature significance analysis, stepwise selection, or regularization methods, depending on the exact details of the implementation. 80% of the data from the fourteen parameters were selected as the training data, while the remaining data was chosen as the testing data. The testing data was then utilized to create the ROC curve.

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