Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices

Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water

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

Application of machine learning to agricultural soil data

Agriculture is a major sector in the Indian economy. One key advantage of classification and prediction of soil parameters is to save time of specialized technicians developing expensive chemical analysis. In this context, this PhD thesis has been developed in three stages: 1. Classification for soil data: we used chemical soil measurements to classify many relevant soil parameters: village-wise fertility indices; soil pH and type; soil nutrients, in order to recommend suitable amounts of fertilizers; and preferable crop. 2. Regression for generic data: we developed an experimental comparison of many regressors to a large collection of generic datasets selected from the University of California at Irving (UCI) machine learning repository. 3. Regression for soil data: We applied the regressors used in stage 2 to the soil datasets, developing a direct prediction of their numeric values. The accuracy of the prediction was evaluated for the ten soil problems, as an alternative to the prediction of the quantified values (classification) developed in stage 1

Determinación de la influencia potencial del suelo en la diferenciación de la productividad y en la clasificación de áreas susceptibles a la marchitez del banano en Venezuela

Banana, the edible fruit of Musaceae, is a staple food for more than 400 million people worldwide due to their nutritional and energy attributes. This makes Musaceae a crop of worldwide relevance, particularly in tropical regions, highlighting the impact of improved Musaceae cropping systems in the current efforts worldwide oriented towards a new agricultural revolution based on sustainable intensification. To achieve this, better practices for food production based on scientific and technical research capable to consider the complexity and variability within the agri-food sector are necessary. The research presented in this PhD Thesis is oriented towards providing answers to the causes of two aspects considered of high relevance for banana production, both affecting productivity and sustainability, always addressed for the Venezuelan conditions, one of the world’s largest producing countries: 1- The impact of phytosanitary risks related to Fusarium wilt and the influence of the soil on the incidence of Banana Wilt (BW) caused by a fungal-bacterial complex. 2- An observed trend towards loss of productivity and decline of soil quality in some commercial farms of Aragua and Trujillo states in Venezuela. The first issue, related to banana plant health, has been covered in two consecutive studies. Firstly, in Chapter I a systematic review on the effect of agro-environmental factors on the impact of Fusarium Wilt of Bananas, caused by Fusarium oxysporum f. sp. cubense (Foc) tropical race 4 (TR4), and the implications for the Venezuelan production system of this disease is presented. This Chapter synthetically characterizes reliable information on the biotic and abiotic factors related to Foc TR4 occurrence, in conjunction with a risk analysis and climate suitability maps for Foc TR4 in Venezuela. This chapter can serve as a basic summary of the available knowledge for use by plant health technicians and professionals, as well as for other stakeholders concerning disease management. The research oriented towards the plant health issues in banana is completed with the study presented in Chapter II. This chapter analyzes the relationship between soil properties and the incidence of Banana Wilt (BW), a disease of unknow etiology, that is attributed to be caused by a fungal-bacterial complex, in a case study of a commercial banana farm in the state of Aragua in Venezuela, whose incidence has reduced the planted area by more than 35.0% in recent years. The application of the Random Forest algorithm allowed to classify with good precision the incidence of BW in lacustrine soils of Venezuela based on the physical and chemical soil properties, being an effective tool for decision-making in the field. In addition, the use of soil information in banana areas of Venezuela allowed the identification of banana lots with high and low incidence of BW using also the Random Forest algorithm. The model showed that the incidence level (low or high) of Banana Wilt could be distinguished through its relationship with Zn, Fe, K, Ca, Mn and Clay content in the soil. These results can contribute to improve our understanding of the basic mechanisms and progression of BW incidence and identify soil variables that can play a determinant role in predicting risk and evolution of BW in banana farms in tropical lacustrine soils. The second issue, related to the relationship between banana productivity and soil properties, has been covered also in two studies. Chapter III contains the research oriented toward the development of an empirical correlation model to predict productivity based on soil characteristics. Five soil properties were found to have a clear agronomic and environmental importance: Mg, resistance to penetration, total microbial respiration, soil bulk density, and free-living omnivorous nematodes. This model could be used at the field level for the reliable identification of areas of high and low banana productivity in the studied areas of Venezuela. Finally, Chapter IV presents a study which can broaden the usefulness of soil information derived from soil profile descriptions. It validated the hypothesis that it is possible to delimit areas of different productivity within banana farms, in the two main banana producing areas of Venezuela (Aragua and Trujillo states) using soil morphological properties (e.g., soil structure). For this, we developed a model of categorical regression prediction calibrated with soil morphological properties such as biological activity, texture, dry consistency, reaction to HCl and structure type. In the future, if further studies are conducted validating this approach in other environmental conditions, banana productivity could be improved using information which might be already available or can be acquired at a moderate cost using standard soil profile descriptions. This PhD Thesis, has combined a systematic bibliographic review, crop and soil information from a systematic survey of different farm types in Venezuela with soil profile descriptions. Using that information, it has validated the hypothesis that by identifying the abiotic properties of the soil, the predisposition of the banana plant to the BW disease, and the potential productivity of the crop can be predicted. This approach can allow the differentiation of zones with different levels of productivity and BW risk, and as an immediate consequence, avoid areas of high risk or low productivity, or adapt agronomical practices to enhance productivity and sustainability of banana cropping systems in Venezuela.La banana, fruta comestible de las Musáceas, es un alimento básico para más de 400 millones de personas en todo el mundo debido a sus atributos nutricionales y energéticos. Esto hace de las Musáceas cultivos de importancia global, particularmente en regiones tropicales, remarcando la importancia de la mejora de los sistemas de cultivo en Musáceas dentro de los esfuerzos actuales a nivel mundial orientados a una nueva revolución agrícola basada en la sostenibilidad productiva. Para lograrlo, son necesarias buenas prácticas para la producción de alimentos basadas en la investigación científica y técnica capaces de considerar la complejidad y variabilidad dentro del sector agroalimentario. La investigación presentada en esta Tesis Doctoral está orientada a dar respuesta a las causas de dos aspectos considerados de alta relevancia para la producción bananera, que afectan tanto la productividad como la sostenibilidad, siempre dirigidas hacia las condiciones de Venezuela, uno de los principales países productores a nivel mundial: 1- El impacto del riesgo fitosanitario relacionado con la Fusariosis Vascular y la influencia del suelo en la incidencia de la Marchitez del Banano (MB) causada por un complejo fúngico-bacteriano. 2- Una tendencia observada hacia la pérdida de productividad y la disminución de la calidad del suelo en algunas fincas comerciales de los estados de Aragua y Trujillo en Venezuela. El primer tema, relacionado con la sanidad vegetal del banano, se ha abordado en dos estudios consecutivos. En primer lugar, en el Capítulo I se presenta una revisión sistemática sobre el efecto de los factores agroambientales en el impacto de la Fusariosis Vascular del banano, causada por Fusarium oxysporum f. sp. cubense (Foc) raza tropical 4 (TR4), y las implicaciones de esta enfermedad para el sistema de producción venezolano. Este Capítulo caracteriza sintéticamente información fiable sobre los factores bióticos y abióticos relacionados con la ocurrencia de Foc TR4, de forma conjunta al desarrollo de un análisis de riesgos y mapas de idoneidad climática para Foc TR4 en Venezuela. Este capítulo puede servir como un resumen básico del conocimiento disponible para el manejo de la enfermedad para que lo utilicen los técnicos y profesionales de la sanidad vegetal, así como para otras partes interesadas. La investigación orientada a los aspectos fitosanitarios del banano se completa con el estudio presentado en el Capítulo II. Este capítulo analiza la relación entre las propiedades del suelo y la incidencia de la Marchitez del Banano (MB) una enfermedad de etiología desconocida, atribuida a un complejo fúngico-bacteriano, en un estudio de caso de una finca comercial bananera en el estado de Aragua en Venezuela, cuya incidencia ha reducido la superficie plantada en más de un 35,0% en los últimos años. La aplicación del algoritmo Random Forest permitió clasificar la incidencia de MB en suelos lacustres de Venezuela con base a las propiedades físicas y químicas del suelo con buena precisión, siendo una herramienta eficaz para la toma de decisiones en campo. Además, el uso de información de suelos en áreas bananeras de Venezuela permitió la identificación de lotes de banano con alta y baja incidencia de MB utilizando también el algoritmo Random Forest. El modelo mostró que el nivel de incidencia (alta o baja) de la MB se puede distinguir a través de su relación con el contenido de Zn, Fe, K, Ca, Mn y arcilla en el suelo. Estos resultados contribuyen a mejorar nuestra comprensión acerca de los mecanismos básicos y la progresión de la incidencia de MB, e identifican las variables del suelo que pueden jugar un papel determinante en la predicción del riesgo y la evolución de MB en fincas bananeras de suelos lacustres tropicales. El segundo tema, relacionado con la productividad del banano y las propiedades del suelo, también se ha abordado en dos estudios. El Capítulo III contiene la investigación orientada al desarrollo de un modelo de correlación empírico para predecir la productividad del banano en base a las características del suelo. Se encontró que cinco propiedades del suelo tienen una clara importancia agronómica y ambiental: Mg, resistencia a la penetración, respiración microbiana total, densidad aparente del suelo y nematodos omnívoros de vida libre. Este modelo podría utilizarse a nivel de campo para la identificación confiable de áreas de alta y baja productividad bananera en las zonas estudiadas de Venezuela. Finalmente, el Capítulo IV presenta un estudio que puede ampliar la utilidad de la información derivada de las descripciones del perfil del suelo. Se validó la hipótesis de que es posible delimitar áreas de diferente productividad dentro de las fincas bananeras, en las dos principales áreas productoras de banano de Venezuela (estados Aragua y Trujillo) utilizando propiedades morfológicas del suelo (por ejemplo, estructura del suelo). Para ello, se desarrolló un modelo de predicción de regresión categórica calibrado con propiedades morfológicas del suelo tales como actividad biológica, textura, consistencia seca, reacción al HCl y tipo de estructura. En el futuro, si se llevan a cabo más estudios que validen este enfoque en otras condiciones ambientales, la productividad del banano podría mejorarse utilizando información que podría estar ya disponible o puede adquirirse a un costo moderado utilizando descripciones estándar del perfil de suelo. Esta Tesis Doctoral ha combinado una revisión sistemática de literatura, información de cultivos y suelos a partir de un muestreo sistemático de diferentes tipos de fincas en Venezuela con descripciones de perfiles de suelos. Con esa información, se ha validado la hipótesis de que, al identificar las propiedades abióticas del suelo, se puede predecir la predisposición de la planta de banano a la enfermedad de la MB y la productividad potencial del cultivo. Esta aproximación puede permitir la diferenciación de zonas con diferentes niveles de productividad y riesgo de la MB y, como consecuencia inmediata, evitar áreas de alto riesgo o baja productividad, incluso adaptar prácticas agronómicas para mejorar la productividad y sostenibilidad de los sistemas bananeros en Venezuela

Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyWith the advent of machine learning (ML) techniques, various algorithms have been applied in previous studies to develop models for predicting soil fertility status. However, these models are observed to use varying fertility target classes, and variations have been reported in these models' predictive performances. As a result, practical applications of these models for obtaining the most accurate predictions may become hindered. While the weighted voting ensemble (WVE) ML technique can be used to improve soil fertility status prediction by aggregating individual models prediction, guaranteeing finding of an optimal WVE assignment weights is challenging. Whereas a brute exhaustive search procedure can be applied for the mentioned task, there is a lack of exploration on the exploitation of automated classifiers' precise weights combinations as search spaces for successful optimization. This research aims to develop a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated 1EXP(-)Z+ multi-precision weights of hybrid classifiers. Soil chemical properties and ML modeling algorithms for modeling soil fertility status were identified. Base hybrid ML classification models for predicting soil fertility status were evaluated using Tanzania as a case study. Finally, the base ML hybrids WVE models were optimized using brute exhaustive search procedure’s novel developed search spaces generation algorithm for guaranteed optimal solution finding. The research was designed using design science research methodology, with the application of unsupervised machine learning K-mean algorithm with a knee detection method to find the optimal number of soil fertility status target classes, and supervised learning algorithms were applied to model classifiers for those optimal classes. Three soil fertility target classes were identified by clustering technique. The model achieved on test data a predictive accuracy of 98.93%, with respective AUC of 82%, 83%, and 87% for low, medium, and high soil fertility targets classes. Whereas these performances are observed higher compared to models in previous studies, 92% correct classifications were obtained on validation against external unseen laboratory-based tested soil results. Therefore, soil testing laboratories and farmers should consider using the model to smartly manage soil fertility which may lead to improved crop growth and productivity. The government could set agricultural-related policies that require the use of the model by farmers with the provision of agricultural inputs subsidies. Future work could be to develop an integrated real-time web and mobile application for providing farmers with soil fertility status information

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

This research article was published by Preprints (www.preprints.org) 2023The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models

Characterisation of Static Liquefaction of Sand with Different Mixtures of Fines

Liquefaction is a phenomenon of a sudden reduction in shear strength of saturated soils when subjected to undrained static or cyclic loading. Static liquefaction susceptibility of sand with various amounts of fines was investigated using experimental and artificial intelligence approaches. Liquefaction behaviour of sandy soils was considerably dependent on the fines content and fines types. The artificial intelligence results showed that both of ANN and GP could well predict the liquefaction susceptibility of mixtures

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field