Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

2024 ◽  
Vol 84 ◽  
Author(s):  
A. Yousafzai ◽  
W. Manzoor ◽  
G. Raza ◽  
T. Mahmood ◽  
F. Rehman ◽  
...  
Keyword(s):  
Climate Change ◽  
Mean Squared Error ◽  
Forest Growth ◽  
Growth And Yield ◽  
Silver Fir ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Yield Data ◽  
Root Mean Squared Error ◽  
Squared Error

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Mitigating climate change effects on forest growth using planting stock with high adaptive genetic capacity: results from Abies alba (Mill.) provenance trials at the southeastern distribution limit

2020 ◽  
Author(s):  
Georgeta Mihai ◽  
Alin-Madalin Alexandru ◽  
Marius-Victor Birsan ◽  
Ionel Mirancea ◽  
Paula Garbacea ◽  
...  
Keyword(s):  
Climate Change ◽  
Radial Growth ◽  
Abies Alba ◽  
Forest Growth ◽  
Silver Fir ◽  
Climate Change Scenarios ◽  
Planting Stock ◽  
Climate Change Effects ◽  
Recent Climate ◽  
Romanian Carpathians

<p>European silver fir (Abies alba Mill.) is among the most important forestry species in Europe. In Romanian Carpathians, it covers about 5% of the forests area and almost two-thirds of its distribution is located in Eastern Carpathians, which is the southeastern edge of its distribution in Europe.<br>The most recent climate change scenarios for Europe suggest increases in mean annual temperature of 1-4 °C by the end of this century (Meinshausen et al. 2011). In the context of global warming, the populations living at the edge of the species distribution will be the first facing the climate change effects. In these regions, as the southeastern Europe, the main constrains are increasing the temperature, extended drought events and water availability. Forest species are particularly sensitive to climate change because the long life-span of trees does not allow for rapid adaptation to environmental changes (Lindner et al. 2010). <br>In this context, the aim of this study was to analyze the drought response of 51 European silver fir populations from: Romanian Carpathians (26), Austria (4), Germany (3), France (3), Italy (4), Slovakia (3), Czech Republic (3), Poland (1) and Bulgaria (4)  to strong drought events which have occurred in this region, in the last 30 years. The populations are tested in three provenances trials established in Romania, in 1980; two of them being located outside and one within the optimum climatic of species. The most drought years, with severe or extreme drought periods, have been identified based on the standardized precipitation index (McKee et al. 1993). The growth response of the silver fir populations to the drought events was evaluated by calculating four parameters, namely: resistance, recovery, resilience, relative resilience (Lloret et al. 2011). Results reveled that the general trend was towards decrease the stem radial growth of silver fir during the last 30 years. The provenance x year interaction was not significant which means high provenances stability over time. Significant differences were found among silver fir provenances in terms of ring width, latewood proportion, resistance, recovery and resilience in drought years. There are provenances which have highlighted high productivity and high tolerance to drought, which could be used in reforestation work, breeding and conservation programs. The radial growth of silver fir provenances was negative affected by the temperature increase during vegetation period and positive by previous autumn-spring precipitations. Therefore, the forest management strategy to mitigate negative impacts of climate change should be based on the knowledge of the intraspecific genetic variation and selection of the best performing and adapted planting stock for each region.</p>

scholarly journals Efficacy of Trans-geographic Observational Network Design for Revelation of Growth Pattern in Mountain Forests Across Europe

2021 ◽  
pp. 141-187
Author(s):  
H. Pretzsch ◽  
T. Hilmers ◽  
E. Uhl ◽  
M. del Río ◽  
A. Avdagić ◽  
...  
Keyword(s):  
Climate Change ◽  
Norway Spruce ◽  
Large Scale ◽  
Statistical Evaluation ◽  
European Beech ◽  
Growth And Yield ◽  
Silver Fir ◽  
Mountain Forests ◽  
Mixed Species ◽  
Yield Data

AbstractUnderstanding tree and stand growth dynamics in the frame of climate change calls for large-scale analyses. For analysing growth patterns in mountain forests across Europe, the CLIMO consortium compiled a network of observational plots across European mountain regions. Here, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir.First, we sketch the state of the art of existing monitoring and observational approaches for assessing the growth of mountain forests. Second, we introduce the design, measurement protocols, as well as site and stand characteristics, and we stress the innovation of the newly compiled network. Third, we give an overview of the growth and yield data at stand and tree level, sketch the growth characteristics along elevation gradients, and introduce the methods of statistical evaluation. Fourth, we report additional measurements of soil, genetic resources, and climate smartness indicators and criteria, which were available for statistical evaluation and testing hypotheses. Fifth, we present the ESFONET (European Smart Forest Network) approach of data and knowledge dissemination. The discussion is focussed on the novelty and relevance of the database, its potential for monitoring, understanding and management of mountain forests toward climate smartness, and the requirements for future assessments and inventories.In this chapter, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir. We present how to acquire and evaluate data from individual trees and the whole stand to quantify and understand the growth of mountain forests in Europe under climate change. It will provide concepts, models, and practical hints for analogous trans-geographic projects that may be based on the existing and newly recorded data on forests.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

scholarly journals Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 248
Author(s):  
Tyler Searls ◽  
James Steenberg ◽  
Xinbiao Zhu ◽  
Charles P.-A. Bourque ◽  
Fan-Rui Meng
Keyword(s):  
Climate Change ◽  
Model Validation ◽  
21St Century ◽  
Black Spruce ◽  
Abies Balsamea ◽  
Acer Rubrum ◽  
Forest Growth ◽  
Balsam Fir ◽  
Growth And Yield ◽  
Climate Projections

Models of forest growth and yield (G&Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&Y projections more robust for use under periods of climate change.

Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

2021 ◽  
pp. 1-21
Author(s):  
Elsa Arrua-Duarte ◽  
Marta Migoya-Borja ◽  
Igor Barahona ◽  
Lena C. Quilty ◽  
Sakina J. Rizvi ◽  
...  
Keyword(s):  
Rating Scale ◽  
Mean Squared Error ◽  
Intraclass Correlation ◽  
Test Validity ◽  
Wilcoxon Test ◽  
Digital Version ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Digital Format ◽  
Paper And Pencil

Abstract Objective: The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Methods: 69 patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales (Kappa and Intraclass Correlation Coefficients), and reliability (Cronbach’s alpha and Guttman’s coefficient). We calculated the Comparative Fit Index and the Root Mean Squared Error associated with the proposed one-factor structure. Results: Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the Intraclass Correlation Coefficient for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F= 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and Root Mean Squared Error was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. Conclusion: The digital DARS is consistent in many respects to the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

scholarly journals Climate Change Influences Basidiome Emergence of Leaf-Cutting Ant Cultivars

2021 ◽  
Vol 7 (11) ◽  
pp. 912
Author(s):  
Rodolfo Bizarria ◽  
Pepijn W. Kooij ◽  
Andre Rodrigues
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Nest Architecture ◽  
Historical Records ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Data Analyses ◽  
Symbiotic Mutualism ◽  
Leaf Cutting

Maintaining symbiosis homeostasis is essential for mutualistic partners. Leaf-cutting ants evolved a long-term symbiotic mutualism with fungal cultivars for nourishment while using vertical asexual transmission across generations. Despite the ants’ efforts to suppress fungal sexual reproduction, scattered occurrences of cultivar basidiomes have been reported. Here, we review the literature for basidiome occurrences and associated climate data. We hypothesized that more basidiome events could be expected in scenarios with an increase in temperature and precipitation. Our field observations and climate data analyses indeed suggest that Acromyrmex coronatus colonies are prone to basidiome occurrences in warmer and wetter seasons. Even though our study partly depended on historical records, occurrences have increased, correlating with climate change. A nest architecture with low (or even the lack of) insulation might be the cause of this phenomenon. The nature of basidiome occurrences in the A. coronatus–fungus mutualism can be useful to elucidate how resilient mutualistic symbioses are in light of climate change scenarios.

scholarly journals How random are predictions of forest growth? The importance of weather variability

2020 ◽  
Author(s):  
Joanna Horemans ◽  
Olga Vindušková ◽  
Gaby Deckmyn
Keyword(s):  
Climate Change ◽  
Uncertainty Analysis ◽  
Climate Models ◽  
Global Climate ◽  
Basal Area ◽  
Random Order ◽  
Forest Growth ◽  
Global Climate Models ◽  
Growth And Yield ◽  
Weather Variability

Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of model studies. We perform such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focus on the effect of the inter-annual climate variability. For that, the climate years in the model input (daily resolution) were randomly shuffled within each 5-year period. In total, 540 simulations (10 parameter sets, 9 climate shuffles, 3 global climate models and 2 mitigation scenarios), were made for one growing cycle (80 years) of a Scots pine forest growing in Peitz (Germany). Our results show that, besides the important effect of the parameter set, the random order of climate years can significantly change results such as basal area and produced volume, and the response of these to climate change. We stress that the effect of weather variability should be included in the design of impact model ensembles, and the accompanying uncertainty analysis. We further suggest presenting model results as likelihoods to allow risk assessment. For example, in our study the likelihood of a decrease in basal area of >10% with no mitigation was 20.4%, while the likelihood of an increase >10% was 34.4%.

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

scholarly journals Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America

2020 ◽  
Vol 12 (18) ◽  
pp. 3098
Author(s):  
Jongmin Park ◽  
Barton A. Forman ◽  
Rolf H. Reichle ◽  
Gabrielle De Lannoy ◽  
Saad B. Tarik
Keyword(s):  
North America ◽  
Brightness Temperature ◽  
Mean Squared Error ◽  
Transfer Model ◽  
Vegetation Types ◽  
Large Variability ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
L Band ◽  
Soil Hydraulic

L-band brightness temperature (Tb) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in Tb observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, Tb estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily Tb observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed Tb were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed Tb with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled Tb due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

Sign in / Sign up

Export Citation Format

Share Document