Innovative Approaches for Environmental and Natural Hazard Forecasting: Proposals from Theory to Practice

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Environmental Forecasting".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 20592

Special Issue Editor

Special Issue Information

Dear Colleagues,

The prediction of future natural or anthropic catastrophes is one of the greatest scientific challenges of our society in the 21st century. Territorial management of protected spaces or densely populated urban areas requires anticipating possible dangers. Mitigation of the risk of fires, floods, or earthquakes, among others, is a discipline in which advances in new prediction tools are made every day. This Special Issue seeks contributions involving innovative approaches or relevant case studies regarding environmental anthropic dangers and natural hazard forecasting in topics such as:

- Desertification and drought of semi-arid regions

- Loss of natural values of protected spaces

- Increased risks associated with climate change

- Wildfire danger in forests and periurban areas

- Assessment of future flood risks associated with anthropogenic actions

- Analysis of the seismic vulnerability of urban areas

Innovative methodologies, frameworks, or significant results from relevant case studies related to all these topics are welcome, but similar ones may also be considered for publication if they fit within the scope of this Special Issue.

Dr. Salvador García-Ayllón Veintimilla
Guest Editor

Manuscript Submission Information

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Keywords

  • environmental forecasting
  • natural hazard risks
  • earthquake vulnerability
  • flooding
  • wildfire danger
  • climate change prediction

Published Papers (6 papers)

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Research

23 pages, 6743 KiB  
Article
Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow
by Andre D. L. Zanchetta and Paulin Coulibaly
Forecasting 2022, 4(1), 126-148; https://doi.org/10.3390/forecast4010007 - 23 Jan 2022
Cited by 9 | Viewed by 3749
Abstract
Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational [...] Read more.
Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime. Full article
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18 pages, 8496 KiB  
Article
Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data
by Guoqi Qian, Antoinette Tordesillas and Hangfei Zheng
Forecasting 2021, 3(4), 850-867; https://doi.org/10.3390/forecast3040051 - 12 Nov 2021
Cited by 1 | Viewed by 2428
Abstract
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector [...] Read more.
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance. Full article
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11 pages, 1363 KiB  
Article
Assessing Goodness of Fit for Verifying Probabilistic Forecasts
by Tae-Ho Kang, Ashish Sharma and Lucy Marshall
Forecasting 2021, 3(4), 763-773; https://doi.org/10.3390/forecast3040047 - 27 Oct 2021
Cited by 6 | Viewed by 2926
Abstract
The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a [...] Read more.
The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation. Full article
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21 pages, 3046 KiB  
Article
Deterministic-Probabilistic Approach to Predict Lightning-Caused Forest Fires in Mounting Areas
by Nikolay Baranovskiy
Forecasting 2021, 3(4), 695-715; https://doi.org/10.3390/forecast3040043 - 27 Sep 2021
Cited by 7 | Viewed by 2532
Abstract
Forest fires from lightnings create a tense situation in various regions of states with forested areas. It is noted that in mountainous areas this is especially important in view of the geophysical processes of lightning activity. The aim of the study is to [...] Read more.
Forest fires from lightnings create a tense situation in various regions of states with forested areas. It is noted that in mountainous areas this is especially important in view of the geophysical processes of lightning activity. The aim of the study is to develop a deterministic-probabilistic approach to predicting forest fire danger due to lightning activity in mountainous regions. To develop a mathematical model, the main provisions of the theory of probability and mathematical statistics, as well as the general theory of heat transfer, were used. The scientific novelty of the research is due to the complex use of probabilistic criteria and deterministic mathematical models of tree ignition by a cloud-to-ground lightning discharge. The paper presents probabilistic criteria for predicting forest fire danger, taking into account the lightning activity, meteorological data, and forest growth conditions, as well as deterministic mathematical models of ignition of deciduous and coniferous trees by electric current of a cloud-to-ground lightning discharge. The work uses synthetic data on the discharge parameters and characteristics of the forest-covered area, which correspond to the forest fire situation in the Republic of Altay and the Republic of Buryatia (Russian Federation). The dependences of the probability for occurrence of forest fires on various parameters have been obtained. Full article
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9 pages, 1610 KiB  
Article
Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach
by Matteo Taroni, Giorgio Vocalelli and Andrea De Polis
Forecasting 2021, 3(3), 561-569; https://doi.org/10.3390/forecast3030035 - 6 Aug 2021
Cited by 11 | Viewed by 4088
Abstract
We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. [...] Read more.
We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. We test this methodology against the classical “rolling window” approach using a high-definition Italian seismic catalogue as well as a global catalogue of high magnitudes. The weighted likelihood approach outperforms competing methods, and measures the optimal amount of past information relevant to the estimation. Full article
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20 pages, 39676 KiB  
Article
Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network
by Matteo Picozzi and Antonio Giovanni Iaccarino
Forecasting 2021, 3(1), 17-36; https://doi.org/10.3390/forecast3010002 - 4 Jan 2021
Cited by 15 | Viewed by 3651
Abstract
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even [...] Read more.
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes. Full article
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