The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance

Sinkhole susceptibility mapping: A comparison between Bayes-based machine learning algorithms

Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human-induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land-degradation mitigation plans

Comparison of two 2-D numerical models for snow avalanche simulation

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. The results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application

STOCHASTIC ASSESSMENT OF LANDSLIDE SUSCEPTIBILITY ALONGSIDE “VÍA AL LLANO” HIGHWAY, COLOMBIA

Le frane frequenti lungo la "Via al Llano", una delle più importanti autostrade colombiane, interrompono regolarmente il traffico. Questa rotta cruciale collega Bogotá, la capitale della Colombia, con Villavicencio, la capitale dello stato di Meta, facilitando il trasporto di beni agricoli e industriali e promuovendo lo sviluppo economico regionale attraverso il turismo. La regione circostante la "Via al Llano" è caratterizzata da caratteristiche geologiche come pieghe, faglie, giunti e affioramenti di diverse litologie ed età. Inoltre, pendii ripidi, deforestazione, depositi non consolidati, alte precipitazioni annuali e un paesaggio fortemente sezionato contribuiscono ulteriormente al verificarsi di frane. Pertanto, identificare accuratamente le aree ad alto rischio di frana, in particolare dove la strada interseca, attraverso la modellazione della suscettibilità alle frane, è imperativo.Nonostante studi precedenti, che si basavano prevalentemente sulla modellazione predittiva, risultassero in una correlazione insufficiente con la strada. Pertanto, l'obiettivo di questo studio è migliorare la risoluzione spaziale suddividendo l'area di studio nei cinque comuni attraversati dalla strada: Chipaque, Caqueza, Quetame, Guayabetal e Villavicencio. Per affrontare la complessità dell'area, lo studio ha prima valutato la fattibilità di sviluppare inventari automatici utilizzando dati radiometrici da immagini satellitari ottiche e radar attraverso la piattaforma Google Earth Engine (GEE). In secondo luogo, sono state create mappe pluviometriche dell'area interpolando 15 anni di dati sulle precipitazioni. Inoltre, sono state generate anche mappe geomorfologiche per ciascuno dei cinque comuni, rappresentando un risultato significativo di questa tesi. Queste mappe forniscono informazioni precedentemente non disponibili, essenziali per comprendere i processi naturali regionali e stabilire elementi fondamentali per mappe di rischio e pericolo.Di conseguenza, lo studio ha impiegato la tecnica delle Splines Adattive di Regressione Multivariata (MARS) per modellare la relazione tra le frane e le variabili predittive come altitudine, angolo di pendenza, esposizione, curvatura, litologia, precipitazioni, NDVI. I modelli sono stati rigorosamente calibrati e validati utilizzando dieci campioni di addestramento e dieci campioni di test, valutando le loro prestazioni predittive tramite la curva ROC (AUC). I nostri risultati indicano che le frane sono più probabili intorno ai corsi d'acqua affluenti del Rio Negro, con variabili chiave -Indice di Posizione Topografica (TPI), Indice di Vegetazione Normalizzato (NDVI), elevazione (ELE), precipitazioni (PLV), pendenza (SLO) e litologia (LTL)- che contribuiscono ad accuratezze predittive che vanno dal 74% all'83%.Frequent landslides along the "Via al Llano”, one of the most important Colombian highways, regularly disrupt traffic. This crucial route connects Bogotá, the capital of Colombia, with Villavicencio, the capital of Meta state, facilitating the transportation of agricultural and industrial goods and promoting regional economic development through tourism. The region surrounding the “Via al Llano” is characterized by geological features such as folds, faults, joints, and outcrops of diverse lithologies and ages. Additionally, steep slopes, deforestation, unconsolidated deposits, high annual rainfall, and a highly dissected landscape further contribute to landslides occurrences. Therefore, accurately identifying high-risk landslide areas, particularly where the road intersects, through landslide susceptibility modeling, is imperative.Despite previous studies, which predominantly relied on predictive modeling, resulting in insufficient correlation with the road. Therefore, the aim of this study is to enhance spatial resolution by subdividing the study area into the five municipalities traversed by the road: Chipaque, Caqueza, Quetame, Guayabetal, and Villavicencio. To address the complexity of the area, the study first assessed the feasibility of developing automatic inventories using radiometric data from optical and radar satellite images through the Google Earth Engine (GEE) platform. Secondly, pluviometry maps of the area were created by interpolating 15 years of rainfall data. Additionally, geomorphological maps for each of the five municipalities were also generated, representing a significant outcome of this thesis. These maps provide previously unavailable information, essential for understanding regional natural processes and establishing foundational elements for risk and hazard maps. Consequently, the study employed the Multivariate Adaptive Regression Splines (MARS) technique to model the relationship between landslides and predictor variables such as elevation, slope angle, aspect, curvature, lithology, precipitation, NDVI. The models were rigorously calibrated and validated using ten training and ten test samples, evaluating their predictive performance by the Receiver Operating Curve (AUC). Our findings indicate that landslides are most probable around the tributary streams of the Rio Negro, with key variables -Topographic Position Index (TPI), Normalized Difference Vegetation Index (NDVI), elevation (ELE), precipitation (PLV), slope (SLO), and lithology (LTL)- contributing to predictive accuracies ranging from 74% to 83%

Exploring the geomorphological adequacy of the landslide susceptibility maps: A test for different types of landslides in the Bidente river basin (northern Italy)

Landslide susceptibility modelling is a crucial tool for implementing effective strategies in landslide risk mitigation. A plethora of statistical methods is available for generating accurate prediction images; however, the reliability of these models in terms of geomorphological adequacy is often overlooked by scholars. This critical flaw may result in concealed prediction errors, undermining the trustworthiness of the obtained maps. A key aspect of evaluating the geomorphological soundness of these models lies in factor analysis, specifically considering the correlation of explanatory variables with the final susceptibility score rather than solely focusing on their impact on model accuracy. This study delves into research conducted in the Bidente river basin (Italy) that analyes results obtained from slide, flow, and complex susceptibility models using Weight of Evidence (WoE) and Multivariate Adaptive Regression Splines (MARS) statistical methods. The research critically examines each factor class's role in defining susceptibility scores for different landslide typologies. The comparison between susceptibility maps generated by WoE and MARS for each typology (slide = 0.78; flow = 0.85; complex: 0.79) (slide = 0.78; flow = 0.85; complex: 0.79)reveals good to excellent prediction skill, with MARS demonstrating a 5 % higher performance index. The study emphasises the importance of spatial relationships between variables and landslide occurrences, highlighting that individual classes of variables influence the final susceptibility score based on their combined role with other predictor classes. In particular, in this study, results highlight that lithotecnical and landform classification classes delimit the landslide domain, while topographic attributes (steepness, curvatures, SPI and TWI) modulate the score inside. The proposed approach offers insights into investigating the geomorphological adequacy of landslide prediction images, emphasising the significance of factor analysis in evaluating model reliability and uncovering potential errors in susceptibility maps

Classification and mapping of the woody vegetation of Gonarezhou National Park, Zimbabwe

Within the framework of the Great Limpopo Transfrontier Conservation Area (GLTFCA), the purpose of this study was to produce a classification of the woody vegetation of the Gonarezhou National Park, Zimbabwe, and a map of its potential distribution. Cover-abundance data of woody species were collected in 330 georeferenced relevés across the Park. These data were used to produce two matrices: the first one using the cover-abundance values as collected in five height layers and the second one based on merging the layers into a single cover value for each species. Automatic classifications were produced for both matrices to determine the optimal number of vegetation types. The two classification approaches both produced 14 types belonging to three macro-groups: mopane, miombo and alluvial woodlands. The results of the two classifications were compared looking at the constant, dominant and diagnostic species of each type. The classification based on separate layers was considered more effective and retained. A high-resolution map of the potential distribution of vegetation types for the whole study area was produced using Random Forest. In the model, the relationship between bioclimatic and topographic variables, known to be correlated to vegetation types, and the classified relevés was used. Identified vegetation types were compared with those of other national parks within the GLTFCA, and an evaluation of the main threats and pressures was conducted. Conservation implications: Vegetation classification and mapping are useful tools for multiple purposes including: surveying and monitoring plant and animal populations, communities and their habitats, and development of management and conservation strategies. Filling the knowledge gap for the Gonarezhou National Park provides a basis for standardised and homogeneous vegetation classification and mapping for the entire Great Limpopo Transfrontier Conservation Area

Uncertainty quantification of medium-term heat storage from short-term geophysical experiments using Bayesian Evidential Learning

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and post-field data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements. Plain Language Summary : Geothermal energy can be extracted or stored in shallow aquifers through systems called aquifer thermal energy storage (ATES). In practice, the energy efficiency of those systems is often lower than expected because of the uncertainty related to the subsurface. To assess the uncertainty, a common method in the scientific community is to generate multiple models of the subsurface fitting the available data, a process called stochastic inversion. However this process is time consuming and difficult to apply in practice for real systems. In this contribution, we develop a novel approach to avoid the inversion process called Bayesian Evidential Learning. We are still using many models of the subsurface, but we do not try to fit the available data. Instead, we use the model to learn a direct relationship between the data and the response of interest to the user. For ATES systems, this response corresponds to the energy extracted from the system. It allows to predict the amount of energy extracted with a quantification of the uncertainty. This framework makes uncertainty assessment easier and faster, a prerequisite for robust risk analysis and decision making. We demonstrate the method in a feasibility study of ATES design

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

CRediT authorship contribution statement: Dr. Aman Arora and Dr. Alireza Arabameri have conceptualized the study, prepared the dataset, and optimized the models. Dr. Manish Pandey has helped in writing the manuscript. Prof. Masood A. Siddiqui, Prof. U.K. Shukla, Prof. Dieu Tien Bui, Dr. Varun Narayan Mishra, and Dr. Anshuman Bhardwaj have helped in improving the manuscript at different stages of this work.Peer reviewedPostprin